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Islamic University of Gaza
Dean of Postgraduate Studies
Faculty of Commerce
Department of Business Administration
"Market Acceptance of Cloud Computing in Gaza IT Market
(An Analysis of Market Structure and Price Models)."
"انقبول انسوقي نهحوسبت انسحابيت في سوق غزة نتكنونوجيا انمعهوماث
نماذج انسوق و نماذج انسعر(.")تحهيم
By
Faten Ahmed Abu Dagga
Supervisor
Professor Yousif H. Ashour
A Thesis Submitted in Partial Fulfillment of the Requirements
for the Degree of MBA
1436-2015
ii
iii
ABSTRACT
As an emerging technology and business paradigm, Cloud Computing embeds fairly
large amount of unexplored fields, from technological definition to business models.
While the market of Cloud Computing is expected to expand in the near future, few
studies of the actual market acceptance of the Cloud Computing services are done.
This thesis aims to study the current and future market acceptance of Cloud
Computing regarding the choice of the users and potential users for market structure
and price model, in light of service homogeneity and usage frequency of the IT
services in Gaza IT market.
The study used the descriptive analytical method and utilized both primary and
secondary sources for data collection. The study population is included employees at
Information technology and communication companies in the Gaza that registered
with PITA. 61 of the 70 distributed questionnaires have been retrieved, forming a
recovery percentage of 87.4%.
The results showed that there is a significant relation between (service
homogeneity, usage frequency) of IT service and Market structure of cloud computing
at significance level α = 0.05. Also the results showed that there is a significant
correlation between price model and usage frequency of IT services at significance
level α = 0.05. In addition, the findings stated that there is no significant correlation
between Service homogeneity of IT service and price model at significance level α =
0.05.
The research recommended that the IT companies should adopt Cloud
Computing technology in its operations, which is an attractive technological and
economic option to the companies.
iv
مهخص اندراست
حذ٠ثا ف عا االعاي اخىخ١ا, فا ان اىث١ش أباعخباس احسبت اسحاب١ت رج ش
احساع سق احسبت اخلعاعاي. ف ح١ ا وارج حىخ١اخابا غ١ش عشفت
ع امبي احم١م سق فا ال ٠خذ دساساث , ف لطاع غزة اسحاب١ت ف اسخمب امش٠ب
اعاث حسبت اسحاب١ت . حىخ١ا
ف١ا حسبت اسحاب١ت اسخمب احا ذ لبي اسق حض١ح إ ز اذساست حذف
ضء ف, اسعش١ى اسق رج اسخخذ١ احخ١ اسخخذ١ خعك باخخ١اس٠
سق غزة خىخ١ا ف خذاث حىخ١ا اعاث اسخخذاحشدد اخذت حداس
.اعاث
اثا٠ت اشئ١ست اصادس عذد ع اعخذث اخح١, اصف اح اذساست اسخخذج
اذساست. ز خص١صا صج اسخبات ف األ١ت اصادس ثج ح١ث اعاث؛ ف١دع
ظف ششواث حىخ١ا اعاث االحصاالث اسدت ذ ب١خا. دخع اذساست ٠ش
87.4%.بسبت أ 70 أص اسخبات 61 اسخشداد ح ح١ث
خذاث االسخخذا(اخداس , حاحش ) راث دالت إحصائ١ت ب١ ان عاللت اخائح أ أظشث
وا اشاسث . α =0.05 سخ اذالت ف احسبت اسحاب١ت سق ١ى حىخ١ا اعاث
حىخ١ا اعاث اسخخذا خذاثح١شة اسعش رج ب١ اخخ١اس اسحباط وب١ش خد اخائح
حداس اسحباط ب١ أ ال ٠خذ اخائح روشث باإلضافت إ ره, α .= 0.05 دالت سخ عذ
α .= 0.05 سخ اذالت اسعش عذ رج خذاث حىخ١ا اعاث
ف اسحاب١ت احسبت خبب ششواث حىخ١ا اعاث ف لطاع عزة حص اذساست
.ششواث االفض اخ١اس اخىخ االلخصاد ,ع١احا
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ACKNOWLEDGEMENTS
By the Name of Allah, the Most Gracious and the Most Merciful
First, I would like to express my appreciation to Allah, the Most Gracious and
the Most Merciful, and the Most Compassionate who has granted me the ability
and willing to start and complete this dissertation.
My most profound thankfulness goes to my supervisor Prof. Dr. Yousif
Ashour, Professor of Operations Research at Islamic University in Gaza, for his
constant support, guidance, and knowledge as well as his unending patience. I
am gratefully and deeply thank him for his support and cooperation as being
equipped to provide his best help. Without such kind of support and
supervision the emergence of this dissertation would have been next to
impossible. I would not forget Dr. Wasim Al-Habil and Dr. Yousef Homodah
for accepting to discuss this research; also I thank Dr. Nafed Barakat for
helping me in statistical analysis.
Special thanks to those noble people who helped me during data collection
stage and validation stage and everyone who put the hand either directly or
indirectly to complete this dissertation.
Last and least, I wish to thank all my dearest family member, especially
my father soul, to my dear mother. The deepest appreciation is expressed to
my husband, for his silent sacrifice and endless support throughout this long
journey as a graduate student.
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DEDICATION
TO MY MOTHER . . .
WITH LOVE AND APPRECIATION
TO THE FUTURE OF PALESTINE . . .
MY SON & DAUGHTER. . .
Ahmed & Ranim
TO MY BELOVED HUSBAND …
WHOMWITHOUT HIM I COULD
NOT FINISH THIS WORK
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ABBREVIATION
Abbreviation Description
AWS Amazon Web Service
CA Combinatorial Auction
CIO Chief Information Officer
CPUs Computing Power Unit
CSPs Cloud Services Providers
DRM Distributed Resource Management
IaaS Infrastructure as a Service
IHS Information Handling Services
IPM Initial Price mode.
IT Information Technology
NIST National Institute of Standards and Technology
OS Operating system
P2P Peer-to-Peer
PaaS Platform as a Service
PAYG
Pay-As-You Go
PCBS Palestinian Central Bureau of Statistics
QoS Quality of Service
RQ Research questions
RSAPAM Resource Swarm Algorithm Price Adjustment model
SaaS Software as a Service
SLAs Service-Level Agreement
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SME Small and Medium-sized Enterprises
SOA Service Oriented Architecture
SPs Service Providers
SPSS Statistical Package for the Social Sciences
UTAUT Unified Theory of Acceptance and Use of Technology
VMM Virtual Machine Monitor
VMs Virtual Machine
VPN Virtual Private Network
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TABLE OF CONTENTS
Item No.
Holy Quran Verse ii
Abstract iii
Arabic Abstract iv
Acknowledgement v
Dedication vi
Abbreviation vii
Table of Contents ix
List of Tables xiv
List of Figures ix
CHAPTER 1: General Introduction
1.1 Introduction 2
1.2 Research Problem Statement 4
1.3 Research Question 5
1.4 Research Variables 5
1.5 Research Hypotheses 6
1.6 Research Objectives 6
1.7 Research Importance 7
1.8 Previous Studies 7
1.9 Research Distinction 14
x
CHAPTER 2: Term Definitions and Classification
2.1 Introduction 16
2.2Cloud Computing 17
2.2.1 What is Cloud Computing 17
2.2.2 Comparing with Virtualization: 21
2.2.3 Comparing with Grid Computing 24
2.2.4 Comparing with Utility Computing 26
2.3 Market participants in the Cloud Computing business 27
2.4 Market structure 39
2.5 Pricing models 30
2.6 Homogeneity of Cloud Computing Services 32
CHAPTER 3: Theoretical Groundwork and Frameworks
3.1 Current Market overview 35
3.1.1Genral: 35
3.1.2 Service Provider (including Service intermediate) 36
3.1.2.1 Pyramid Model of Cloud Computing Market 36
3.1.2.2 Service Providers in Cloud Computing Market: 39
3.1.3 Service Buyer 43
3.2 Research Status 46
3.2.1 Theoretical Groundwork and Frameworks for Market Structure 46
3.2.1.1 General 46
3.2.1.2 Public Cloud, Private Cloud, and Hybrid Model 46
3.2.1.3The Transaction Cost Theory: 50
xi
3.2.1.4 Physical asset Specificity and Service Homogeneity 54
3.2.2 Theoretical Groundwork and Frameworks for price Model 54
3.2.2.1 General 54
3.2.2.2 PAYG, Flat Rate and Mixture Model 56
3.2.2.3 Service Homogeneity and Price Model 58
3.2.2.4 Usage Frequency and Price Model 59
CHAPTER 4: Methodology
4.1 Introduction 62
4.2 Research Design 62
4.3 Research Methodology 63
4.3.1 Data Collection Methodology: 63
4.3.1.1 Secondary data 63
4.3.1.2 Primary data 64
4.3.2 Questionnaire content 64
4.3.2.1 Questionnaire structure 64
4.3.3 Population and Sampling 65
4.4 Pilot Study 66
4.5 Methodology of data analysis 66
4.5.1 Data preparation 66
4.5.2 Statistical Analysis Tools 67
4.6 Tests of Normality 67
4.7 Validity of the Research 68
4.7.1 Content Validity of the Questionnaire 68
xii
4.7.2 Statistical Validity of the Questionnaire 68
4.7.3 Criterion Related Validity 69
4.7.3.1 Internal consistency: 69
4.7.4 Structure Validity of the Questionnaire 73
4.8 Reliability of the Research 74
4.8.1 Cronbach’s Coefficient Alpha 74
4.8.2 Half Split Method 75
CHAPTER 5: RESEARCH ANALYSIS AND FINDINGS
5.1 The first dimensions (general information) 77
5.1.1 Knowledge about Cloud Computing 77
5.1.2 IT-related investments 77
5.1.3 Current market acceptance of Cloud Computing 78
5.1.4 Reason for using Cloud Computing services 79
5.1.5 Reason against using Cloud Computing services 81
5.2 Hypothesis #1 Test (Test Statistical description of the study population) 83
5.2.1 Gender 83
5.2.2 Qualification 84
5.2.3 Age 85
5.2.4 Field of Specialization 86
5.2.5 Position 87
5.2.6 Years of Experience at this company 88
5.2.7 Department 89
5.3 Hypothesis #2 Test 90
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5.3.1 Hypothesis 1 90
5.3.2 Hypothesis 2 91
5.3.3 Hypothesis 3 93
5.3.4 Hypothesis4 95
6.1 Introduction: 98
6.2 Research Results 98
6.2.1 Questionnaire Paragraphs 98
6.2.2 Hypothesis Testing Results 101
6.2.3 Answers of research questions 101
6.3 Evaluation of research methodology 102
6.4 Concluding Remarks and Further Research Directions 102
References
Appendices
xiv
List of Tables
Table
No. Table Name
Page
No.
2.1 Essential Characteristics, Service Models, and Deployment
Models of cloud 19
3.1 The 38 most active SPs in current Cloud Computing market. 41
3.2 Comparison of Public Cloud, Private Cloud and Hybrid Model
49
3.3 Matching Market Structures with Asset Specificity and
Frequency 53
3.4 Classification of different payment structures 57
3.5 Pricing Model 57
4.1 1-Sample k-s 67
4.2
The correlation coefficient between each question in the field
and the whole field (Why cloud computing seems attractive to
your company include?)
69
4.3
The correlation coefficient between each question in the field
and the whole field (Your concern(s) about using Cloud
Computing now or in near future is/are)
70
4.4
The correlation coefficient between each question in the field
and the whole field (service homogeneity of IT service)
71
4.5
The correlation coefficient between each question in the field
and the whole field (usage frequency of IT service)
71
4.6
The correlation coefficient between each question in the field
and the whole field (Market structure)
72
xv
4.7
The correlation coefficient between each question in the field
and the whole field (Price model)
73
4.8 Structure Validity of the Questionnaire
73
4.9 Cronbach's Alpha For Reliability
74
4.10 Split-Half Coefficient method
75
5.1 Independent Samples Test for differences about market
acceptance of cloud computing in Gaza it market refer to gender 83
5.2 One way ANOVA test for differences about market acceptance
of cloud computing in Gaza it market refer to Qualification 84
5.3 One way ANOVA test for differences about market acceptance
of cloud computing in Gaza it market refer to Age 85
5.4
One way ANOVA test for differences about market acceptance of
cloud computing in Gaza it market refer to Field of Specialization
86
5.5 One way ANOVA test for differences about market acceptance of
cloud computing in Gaza it market refer to Position 87
5.6 One way ANOVA test for differences about market acceptance of
cloud computing in Gaza it market refer to Years of Experience 88
5.7 One way ANOVA test for differences about market acceptance of
cloud computing in Gaza it market refer to Department 89
5.8 Service homogeneity * Market structure Crosstabulation 90
5.9 Chi-Square Tests 91
5.10 Usage frequency * Market structure Crosstabulatio 92
5.11 Chi-Square Tests 92
xvi
5.12 Service homogeneity * price model Crosstabulation 94
5.13 Chi-Square Tests 94
5.14 usage frequency* price model Crosstabulation 95
5.15 Chi-Square Tests 96
xvii
List of Figures
Figure
No.
Figure Name
Page
No.
2.1 Cloud Computing diagram 18
2.2 Actors in the Service Cloud Market 19
2.3 Ranking of technologies CIOs 22
2.4 Grid architecture 24
2.5 Global Cloud exchange and market infrastructure for trading
services. 28
3.1 Cloud- Related Spending by Businesses to Triple from 2011 to
2017 35
3.3 “Cloud Pyramid”: Layered Structure of Cloud Computing Services 36
3.4 Public Cloud 47
3.5 Private Cloud 47
3.6 Hybrid Cloud 48
4.1 Illustrates the methodology flow chart 63
5.1 Corresponding Companies’ IT budgets in Percentage of Total
Revenue from Previous Year (2013) 78
5.2 The Current Acceptance of Cloud Computing Services 79
5.3 Reasons of Using Cloud Computing Services 80
5.4 Concerns of Using Cloud Computing Services 81
5.5 Service homogeneity * Market structure 91
5.6 Usage frequency * Market structure 93
5.7 Service homogeneity * price model
94
5.8 Usage frequency*price model
96
1
Chapter 1
General Introduction
Chapter Outline:
1.1 Introduction
1.2 Research Problem Statement
1.3 Research Question
1.4 Research Variables
1.5 Research Hypotheses
1.6 Research Objectives
1.7 Research Importance
1.8 Previous Studies
1.9 Research Distinction
2
1.1 Introduction
When you store your photos online instead of on your home computer, or use
webmail or a social networking site, you are using a ―Cloud Computing‖ service. If
you are an organization, and you want to use, for example, an online invoicing service
instead of updating the in-house one you have been using for many years, that online
invoicing service is a ―Cloud Computing‖ service.
Nowadays, the term ―Cloud Computing‖ has been an important term in the world of
Information Technology (IT). Cloud Computing, or the use of Internet-based
technologies to conduct business, is recognized as an important area for IT innovation
and investment (Armbrust et al., 2011; Goscinski et al., 2011; Tuncay, 2010).
Cloud Computing is a kind of computing which is highly scalable and use virtualized
resources that can be shared by the users. Users do not need any background
knowledge of the services. Moreover, a user on the Internet can communicate with
many servers at the same time and these servers exchange information among
themselves (Hayes, 2010). Basically, data and applications on Cloud Computing are
available through the Internet, so it can be accessed from everywhere.
Cloud computing popularly termed as the computing system which offers Internet
based services on demand in parallel and distributed environment. It is considered as
one of the emerging IT technology which relies on distributed sharing of resources
over different geographical locations to deliver services efficiently to users upon their
request (Pattnaik et al., 2015).
Additionally, Shalini mentioned that the Internet is the "cloud" of applications and
services that are available for access to subscribers utilizing a modem from their
computer. With Cloud Computing, businesses may prevent financial waste, better
track employee activities, and avert technological headaches such as computer
viruses, system crashes, and loss of data. When Cloud Computing are used in
education, this will likely have a significant impact on teaching and learning
environment me (Shalini, 2012).
According to Spreeuwenberg (2012), with Cloud Computing it becomes easier to
access data with several devices. Especially for mobile devices this can be really
useful since the only thing that is needed, is an Internet connection.
3
Cloud computing get recently the attention of many organizations, including the
capital market and significant benefits from its use in new capital market services.
Cloud computing in providing the amount of resources requested by the users is
flexible. Customers in the cloud are used only for what they have paid their fees.
In Jan 2015, Forrester Research expects Cloud computing to be a $159.3
billion market by 2020 and Gartner Research in the beginning of 2015 prognosticates
a $150 billion clouding business by 2015. The expectations of the business with
Cloud computing are high and no competitor on the IT service market can ignore the
Cloud Computing paradigm.
The main focus of academic researchers at that time was on the "technical" topic, such
as like load balance, resource allocation etc. But the pure technical maturity (given
that is already available) does not necessarily lead to a wide acceptance of a new
technology, because there are other forces and mechanism influencing the market
development of it: on one hand, the market mechanism could probably solve the
resource allocation problems in systems, and on the other hand, a technical trend will
be of little use if it cannot gain enough commercial exposure. One of the best ways to
find out the market acceptance is asking directly the users and potential users of
Cloud Computing services. For this reason, a survey about the attitudes of current and
potential users toward Cloud Computing was designed as a basis research material for
this thesis.
Based on this survey, analyses are done in several aspects including general
knowledge about Cloud Computing, expectations and concerns, service homogeneity
and usage frequency of the services, preferred market structures and price models in
Gaza IT market which faces difficulties in importing equipment and tools needed for
work, not to mention need to travel to introduce international companies to the local
abilities and potential and to enhance trust. And that‘s not currently doable except
with difficulty, which makes work not grow much despite the global growth.
A simple random sampling is applyed for this survey, because the users and potential
users of Cloud Computing services can be any company; even if they aren‘t yet using
any IT services, they can be potential Cloud Computing customers: they can simply
use Cloud Computing services from the very beginning and own no legacy system at
4
all, so samples will be chosen from any company or organization, like universities and
hospitals that use IT services or going to use IT services.
The rest of this thesis is organized as following: Chapter 2 provides a comprehensive
definition of Cloud Computing as well as a comparison with other similar concepts
like Grid Computing and Utility Computing; Chapter 3 gives a review of the status
quo for the current market of Cloud Computing, as well as both theoretical
frameworks related with market structures and price models; Chapter 4 focuses on the
research methodology of this thesis, which mainly includes a survey; at the core of
this paper, Chapter 5 demonstrates the survey results and provide an analyses
regarding the choice of market structure and price model, based on the survey results,
Chapter 6 gives the results of the study and some further research directions.
1.2 Research Problem Statement:
So far, at home and abroad, most of the studies about ―cloud‖ are still rest on the
technology level, e.g., the strategy and solution of cloud system architecture, cloud
application, cloud security, encryption, privacy protection, access control and other
issues. For example, Lim et al, (2013) proposes an overview of the new proposed
Cloud Computing reference architecture but focusing on one of the cloud provider
components which is cloud service management.
Additionally, Chandio et al, (2015) proposes a novel technique that will not leave
consumer alone in cloud environment. There is a presentation of theoretical analysis
of selected state of the art technique and identified issues in IaaS (Infrastructure as a
service) Cloud Computing. In addition to that, the study proposes Distributed Trust
Protocol for IaaS Cloud Computing in order to mitigate trust issue between cloud
consumer and provider.
Also (Narula et al, 2015) which provides the review of security research in the field of
cloud security. After that, it has presented the working of AWS (Amazon Web
Service) Cloud Computing. AWS is the most trusted provider of Cloud Computing
which not only provides the excellent cloud security but also provides excellent cloud
services.
5
Behavior Intention is considered to be an important indicator to forecast potential
users‘ acceptance of new technology/ services, however, from the search results on
Science Direct, Emerald and CNKI, we find that the study on cloud service user
adoption is still very rare. While the Cloud Computing technology is gaining ever
more attention from the public, the variety of Cloud Computing services, including
forms of market coordination, price models, service level requirements etc., is
growing too.
So the main propose of this thesis is to study the current and future market
acceptance of Cloud Computing regarding the choice of market structure and price
model, in light of service homogeneity and usage frequency of the IT services in Gaza
IT market.
1.3 Research Questions
The study deals with the analysis of the Cloud Computing market in terms of market
structure and price models to determine the degree of market acceptance of Cloud
Computing. Hence, the research question will be:
What is the degree of market acceptance of Cloud Computing in Gaza IT market?
For the research question, there are sub-questions defined that help to oversee the
steps to achieve a similar answer to the research question.
1.4 Research Variables:
The dependent variables:
The main variable:
Market acceptance of Cloud Computing
The sub-variables:
a. Market structure of Cloud Computing services.
b. Price model of Cloud Computing services.
The independent variable:
a. The homogeneity of Cloud Computing services.
b. The usage frequency of Cloud Computing services.
6
1.5 Research Hypotheses:
There are two main hypotheses for this research:
1. There are significant statistical differences at significant level (α≤0.05) among the
respondents' answers regarding market acceptance of Cloud Computing due to
personal traits (Gender, Age, Qualifications, Type of Position, Position and Years of
Experience).
2. There is a significance effect between independent variables (The homogeneity and
the usage frequency of Cloud Computing services) and Market Acceptance of
Cloud Computing in Gaza IT market (at level of significance α≤0.05).
From this main hypothesis the following sub hypotheses result:
a. There is a statistical significant relation between the service homogeneity of
Cloud Computing services and the market structure of Cloud Computing
services (at level of significance α≤ 0.05).
b. There is a statistical significant relation between the usage frequency of Cloud
Computing services and the market structure of Cloud Computing services (at
level of significance α≤0.05).
c. There is a statistical significant relation between the service homogeneity of
Cloud Computing services and the price model of Cloud Computing services (at
level of significance α≤0.05).
d. There is a statistical significant relation between the usage frequency of Cloud
Computing services and the price model of Cloud Computing services (at level of
significance α≤0.05).
1.6 Research Objectives:
The main objective of this research is to overcome the problem statement, for that this
research is being carried out with several objectives and it is important to state them
clearly, to ensure that the research is kept on track.
7
The second main objective of this research is to determine the degree of Market
Acceptance of Cloud Computing in Gaza which can be divided to the following sub
objectives:
a. To find out the potential influences of service homogeneity of Cloud
Computing on customer‘s choice of market structures.
b. To find out the potential influences of usage frequency of Cloud Computing
on customer‘s choice of market structures.
c. To find out the potential influences of service homogeneity of Cloud
Computing on customer‘s choice of price model.
d. To find out the potential influences of usage frequency of Cloud Computing
on customer‘s choice of price model.
1.7 Research Importance:
The importance of this study considered as the first empirical study in the market
acceptance of Cloud Computing services regarding the market structures and price
models in Gaza.
This thesis represents clearly the customer‘s point of view rather than technical or
architectural requirements. It is not to say that technical and architectural
requirements are not important, but what the customers pay most attention to are the
benefits they can get from the technology. For example, a real-time delivery of
products and services is more important than whether the products and services are
provided via Peer-to-Peer (P2P) network, Virtual Private Network (VPN) network or
direct via Internet.
1.8 Previous Studies
Cloud Computing has been examined from different perspectives and through
different research strategies. This section will shed more lights on some of significant
studies that took place in different countries in the world; many other studies were
referred to and linked with through the thesis:
8
1. (Alharbi, 2014) ''Trust and Acceptance of Cloud Computing: A Revised
UTAUT Model''
This paper carried out in Saudi Arabia to propose a revised Unified Theory of
Acceptance and Use of Technology (UTAUT) for Cloud Computing acceptance
taking into account trust as a main construct in the model. The UTAUT is one of the
most widely used model for investigating the acceptance of information technology
and explaining factors influencing users accepting of information technology. Its
validity has been demonstrated in wide range of information system contexts.
A survey including the proposed statements, distributed to software engineers
enterprises that deals with Cloud Computing services.
The author concludes the adopting Cloud Computing technology is still facing various
challenges mainly establishing trust. The UTAUT is one of the most widely used
model for investigating the acceptance of information technology and explaining
factors influencing users accepting of information technology
The author suggests statements related to trust categorized in various trust aspects.
The validity of the proposed model will be investigated in a further study.
2. (Stieninger et al, 2014) "Diffusion and Acceptance of Cloud Computing in
SMEs: Towards a Valence Model of Relevant Factors"
This paper carried out in Germany to propose some factors which influence the
diffusion and acceptance of Cloud Computing within organizations. The following
two research questions (RQ) are assessed by this paper:
Which influencing factors are addressed by scientifically proven theory
models concerning diffusion and acceptance of technological innovations?
What is the relevance of the influencing factors deduced from the theory
models on the attitude of SMEs towards Cloud Computing?
The authors combine the theoretical approach from scientifically recognized literature
with a practical evaluation of influences on the diffusion and acceptance of Cloud
Computing among SMEs. The analyzed theory models cover four main areas of
influence on acceptance and diffusion of technological innovations: The individual,
the organization, the technology and the environment. Factors from these established
9
theory models dealing with acceptance and diffusion of innovations served as a vital
basis for the data analysis process.
A survey including the proposed statements, distributed to 436 German cloud
omputing supporters companies.
The authors conclude that a valence model was created which sheds light on the
differing relevance of influencing factors in both positive and negative directions.
Thereby it provides a broad overview of essential areas and perspectives which have
to be considered from the viewpoint of an SME.
3. (Huang et al, 2014) "Pricing strategy for cloud computing: A damaged
services perspective"
This paper carried out in Singapore to propose whether interruptible spot-price on
demand Cloud Computing services—which are viewed as damaged services—are
valuable to the vendor.
Three types of pricing: fixed prices for reserved services, spot prices for on-demand
services, and a mixture of them in a hybrid strategy are compared.
The authors conclude that a vendor should employ a hybrid strategy, but only when:
clients are sensitive to services interruptions; or task values are highly differentiated.
The vendor may be able to increase its profit by keeping the services interruption risk
at a recognizable and substantial level, so that the cannibalization effect between
fixed-price services and spot-price services is minimized On the other hand, using a
hybrid strategy will enhance the vendor's price discrimination ability: it can segment
the market by both client demand and task value — leading to lower consumer
surplus in most cases.
The authors offered some recommendations:
The vendor, when employing a hybrid strategy, should version the spot price
services by introducing interruption risk. The presence of interruptions enables
the vendor to implement price discrimination and also gain resource allocation
flexibility.
For hybrid pricing strategy with damaged services to be beneficial to the
vendor, clients must be sensitive to services interruptions.
To gain more profit, the vendor should not minimize the risk of services
interruption for spot-price services.
11
With competition, a vendor may consider hedging interruption risk for its
clients with tools to help them overcome interruption impacts.
A vendor that employs a hybrid strategy with damaged services can further
improve its profit by imposing a limit in the capacity associated with fixed-
price non-interruptible reserved-services contracts.
4. (Zhang et al, 2014) "Price Competition in a Duopoly IaaS Cloud Market"
This paper carried out in Japan to propose how to set optimal prices in order to
maximize the revenue of CSPs (Cloud services providers) in a competitive IaaS Cloud
Computing market while at the same time meeting the cloud users‘ demand
satisfaction is a problem that CSPs should consider. Because of that they study
subscription pricing competition in a duopoly IaaS Cloud Computing market. They
also present a game theoretic analysis of a cloud market with two CSPs competing
non-cooperatively for cloud users.
The study presents a game theoretic analysis of a cloud market with two CSPs
competing non-cooperatively for cloud users.
The authors conclude that, when there are homogeneous Cloud Service Providers, i.e.,
the two CSPs have the same capacities, both CSPs will charge the same price, and
they have the same market share. The two CSPs are indifferent to cloud users, which
imply that the equilibrium solutions of the homogeneous scenario are symmetric.
The authors recommend focusing on the numerical analysis of the effects of resource
capacities on equilibrium prices and expected revenues in monopoly cloud market and
duopoly cloud market.
5. (Li et al, 2012) "A Cloud Computing Resource Pricing Strategy Research-
based on Resource Swarm Algorithm"
This paper carried out in Chaina to propose a model called Cloud Bank to support the
research of pricing resources. Cloud Bank model is the one of IaaS application. They
combined with relevant principles of economics, Cloud Computing and resource
swarm algorithm to discuss how resource swarm algorithm is applied to the resources
11
price adjustment. Make an analysis to the computing resources pricing in Cloud Bank
model. Put forward a strategy to solve the problem that pricing resources.
Two important models that are Initial Price model (IPM) and Resource Swarm
Algorithm Price Adjustment model (RSAPAM).
The authors improve resource swarm algorithm for enriching the pricing strategy. In
the end, this pricing strategy realizes automatic adjustment of computing resources
price.
6. (Breskovic et al, 2011) "Towards Self-Awareness in Cloud Markets: A
Monitoring Methodology"
This paper carried out in Germany to propose a methodology that could enable a
market platform to be self-aware, i.e. knowledgeable about its state at multiple levels.
The authors utilize GridSim (Buyya, 2011), as a widely used tool for the simulation of
Grid and Cloud market behavior to demonstrate their approach. They have extended
GridSim with appropriate market and mechanism sensors as well as simple
infrastructure sensors. Based upon the monitoring metrics of the market their
monitoring model can sense dynamic changes in market behavior, which is the first
step towards establishing self-aware and self-manageable market platforms.
The simple evaluation scenario (a sudden cease in demand) illustrated that a sudden
change in demand for resources can lead to market instability, and ultimately crashes,
as was painfully demonstrated in the recent financial crisis. This temporarily affected
the performance of market goals (both positively and negatively), and in a real
deployment would have resulted in excessive and costly utilization of unneeded
hardware infrastructure. The authors show that such phenomena can be detected by
that monitoring model, which may in the future help to identify and react to sudden
changes in the performance of Cloud markets such that they can begin to give these
platforms autonomic capabilities and enable them to steer away from and avoid
negative market outcomes.
The authors intend to investigate similar phenomena and tune the monitoring model
accordingly. They also plan to include additional allocation mechanisms for future
studies.
12
7. (Rimal et al, 2010) " A Taxonomy, Survey, and Issues of Cloud Computing
Ecosystems "
This paper carried out in Korea to propose explanations of each of the components
consisting of modes of Cloud Computing services, virtualization management, core
services, security, data governance, and management services.
The authors contribute a great deal of better understanding of the classification of the
Cloud Computing and its applications to further research of similar issue including
this study.
8. (Shang et al, 2010) "A Knowledge-based Continuous Double Auction Model
for Cloud Market"
This paper carried out in China to propose a knowledge-based continuous double
auction trade model. It introduces a probability based on historical trading
information, and use historical bids to determine the probability that future bids will
succeed. With this probability agent can then adjust the bidding or quoting price or
ask price automatically. Combine this probability with profit to estimate how to place
bids to maximize expected profit. If there were many bids made at each price point
than the probability could simply be the number of shouts accepted at a particular
price point.
The authors develop a simulator to test the related feature. The results show that the
model is efficient in resource trading. The mean efficiency of resource trading is
97.770%.That mean that the trading price is more stable. They intend includes
applying that model to a real cloud resource environment and conducting experiments
of larger scale to test the efficiency of that model.
9. (Buyya et al, 2009) "Market-Oriented Cloud Computing: Vision, Hype, and
Reality for Delivering IT Services as Computing Utilities"
This paper carried out in Australia to propose architecture for market-oriented
allocation of resources within Clouds including (Users/Brokers, Service Level
Agreements Resource Allocator, Multiple VMs (Virtual Machine) and Physical
Machines). In addition to that, there is discussion about representative platforms for
Cloud Computing covering the state-of-the-art. Also the paper presents a vision for
the creation of global Cloud exchange for trading services.
13
The authors conclude that the Cloud technologies have limited support for market-
oriented resource management and they need to be extended to support: negotiation of
QoS (Quality of Service) between users and providers to establish SLAs; mechanisms
and algorithms for allocation of VM resources to meet SLAs; and manage risks
associated with the violation of SLAs. Furthermore, interaction protocols needs to be
extended to support interoperability between different Cloud service providers.
The authors recommend that several challenges need to be addressed to realize this
vision. They include: market-maker for bringing service providers and consumers;
market registry for publishing and discovering Cloud service providers and their
services; clearing house and brokers for mapping service requests to providers who
can meet QoS expectations; and payment management and accounting infrastructure
for trading services, Finally, they need to address regulatory and legal issues, which
go beyond technical issues.
10. (Song et al, 2009) "A Novel Cloud Market Infrastructure for Trading
Service"
This paper carried out in South Korea to propose combination of Cloud providers that
minimizes the total service price (including collaboration cost) for consumers and also
reduces conflicts among providers as well as negotiation time while maintaining the
QoS requirements of consumers. Because of that they proposed a novel CA
(combinatorial auction)-based trading infrastructure to enable the supply and demand
of Cloud services by modifying existing auction policy in terms of its suitability,
economic efficiency and system performance. Such market can allow consumers to
choose a set of Cloud providers that suits their requirements. Providers can use the
market in order to perform effective capacity planning. Also this market can provide
feedback in terms of economic incentives for both Cloud consumers and providers.
The authors recommend finding out an appropriate group strategy for the Cloud
providers so that they can make dynamic groups and increase their competitive power
and compete for winning the bid.
14
1.9 Research Distinction
Those studies used different types of methodologies, some of them applied the
analytical descriptive method, and another part of them carried laboratories
pediments, while others used proposed modules. Moreover these studies were
conducted in different types of organizations including the governmental institutions,
public security establishments, and private sector's firms. These studies conducted in
different countries with different societies, environments and cultures.
The applied samples vary in their types. Part of the results that were found throughout
this study come on line with the previous researches and other findings were the
privilege of this study.
This thesis differs from other literature in many other ways. The main contributions of
this thesis are found in following:
a. Focus explicitly on the Cloud Computing services, which are defined clearly
in comparison with other ―Cloud-like‖ technologies, such as Grid Computing,
Utility Computing and so on.
b. Apply certain theoretical frameworks, such as the Transaction Cost Theory, on
the current Cloud Computing market, trying to figure out whether these
existing theories are able to deliver a framework to understand the new Cloud
Computing paradigm.
c. Conduct a survey to test the prediction power of those theoretical frameworks.
d. Provide latest information about the customers and market of Cloud
Computing via this survey, such as the customers‘ concerns about Cloud
Computing services, and the stage of market development etc.
15
Chapter 2
Term Definitions and
Classification
Chapter Outline:
2.1 Introduction
2.2 Cloud Computing
2.2.1 What is Cloud Computing
2.2.2 Comparing with Virtualization:
2.2.3 Comparing with Grid Computing
2.2.4 Comparing with Utility Computing
2.3 Market Participants in the Cloud Computing Business
2.4 Market Structure
2.5 Pricing Models
2.6 Homogeneity of Cloud Computing Services
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2.1 Introduction
The software industry has undergone tremendous changes with the introduction of-
cloud services. Initially various applications were offered as cloud services in what
has become commercially known as the-SaaS (Software as a Service) business model.
Nowadays, not only applications but also computational infrastructure such as CPUs
(computing power unit) and memory (disk space) are offered in a similar service
model (sometimes dubbed-IaaS(Infrastructure as a Service)) by companies such as
Amazon, Microsoft, many telecommunication companies and more.
A recent Gartner report provides some insight into the market size worldwide
(Gartner, 2014):
By 2015, 50% of all new application independent software vendors will be pure
SaaS providers.
Through 2015, more than 90% of private Cloud Computing deployments will be
for infrastructure as a service.
By 2015, 50% of large global enterprises will rely on external Cloud Computing
services for at least one of their top 10 revenue-generating processes.
By 2016, all large global enterprises will use some level of public cloud services.
By 2016, most SaaS contracts will include price escalation limitations and the
ability to terminate contracts.
By 2017, over 50% of large SaaS application providers will offer matching
business process services and an integrated platform as a service.
Through 2017, 5% of all IT job turnover will be fallout from poor risk decisions
about the use of public Cloud Computing.
Through 2017, 80% of large enterprises will restrict their private cloud data center
services to less than 20% of their total data center services.
Through 2020, the most common use of cloud services will be a hybrid model
combining on-premises and external cloud services.
The market for cloud services, and in particular IaaS, has a variety of unique features
which make it different from other markets. One particular aspect is that the goods
17
themselves (memory and CPU) are homogeneous and fully divisible. Thus, the
pricing schemes that could prevail may also be unique to this market.
The main purpose of this thesis is to study the current and future market acceptance of
Cloud Computing. To notice is, before Cloud Computing, there are already several
technical trends with similar characteristics, like Application Service Provider (ASP),
Grid Computing etc. Despite the differences between these technologies, the main
focus of academic researchers at that time was on the "technical" topic, such as like
load balance, resource allocation etc. But the pure technical maturity (given that is
already available) does not necessarily lead to a wide acceptance of a new technology,
because there are other forces and mechanism influencing the market development of
it: on one hand, the market mechanism could probably solve the resource allocation
problems in systems, and on the other hand, a technical trend will be of little use if it
cannot gain enough commercial exposure. One of the best ways to find out the market
acceptance is asking directly the users and potential users of Cloud Computing
services. For this reason, a survey about the attitudes of current and potential users
toward Cloud Computing was designed as a basis research material for this thesis.
Based on this survey, analyses are done in several aspects including general
knowledge about Cloud Computing, expectations and concerns, service homogeneity
and usage frequency of the services, preferred market structures and price models in
Gaza IT market.
2.2 Cloud Computing
2.2.1 What is Cloud Computing?
Cloud Computing is a new subject at both technological and commercial level,
therefore various definitions can be found, focusing on different characteristics of
Cloud Computing technology, services, and platform. So the definition of Cloud
Computing has been defined differently by different industry experts and researchers:
According to Vangie Beal - the Managing Editor of Webopedia.com-, the
word cloud (also phrased as "the cloud") is used as a metaphor for "the
Internet," so the phrase cloud computing means "a type of Internet-based
computing," where different services — such as servers, storage and
18
applications — are delivered to an organization's computers and devices
through the Internet.
According to Armbrust et al. ―Cloud Computing refers to both the applications
delivered as services over the Internet and the hardware and systems software
in the datacenters that provide those services. The services themselves have
long been referred to as Software as a Service (SaaS). The datacenter
hardware and software is what we will call a Cloud. When a Cloud is made
available in a pay-as-you-go manner to the general public, it is called a Public
Cloud; the service being sold is Utility Computing. Authors use the term
Private Cloud to refer to internal datacenters of a business or other
organization, not made available to the general public. Thus, Cloud
Computing is the sum of SaaS and Utility Computing, but does not include
Private Clouds‖ (Armbrust et al., 2011).
Figure 2.1: Cloud Computing diagram (gethackingsecurity, 2014)
From a market point of view "Clouds are a large pool of easily usable and
accessible virtualized resources (such as hardware, development platforms
and/or services). These resources can be dynamically assigned to adjust to a
variable load, allowing also for optimum resource utilization. This pool of
resources is typically exploited by a pay-per-use model in which guarantees
are offered by the Infrastructure Provider by means of customized SLAs
(Service-level agreement) "( Lindner,2009) .
19
Figure 2.2: Actors in the Service Cloud Market (Lindner, 2009)
According to the official NIST definition, "Cloud computing is a model for enabling
convenient, on-demand network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and services) that can be
rapidly provisioned and released with minimal management effort or service provider
interaction. This cloud model is composed of five essential characteristics, three
service models, and four deployment models." (Badger et al, 2012).
Table 2.1 Essential Characteristics, Service Models, and Deployment Models of cloud (NIST,
2012)
Other common academic and scholarly definition defines Cloud Computing as
"an emerging data interactive paradigm to realize users‘ data remotely stored
in an online cloud server. Cloud services provide great conveniences for the
users to enjoy the on-demand cloud applications without considering the local
21
infrastructure limitations. During the data accessing, different users may be in
a collaborative relationship, and thus data sharing becomes significant to
achieve productive benefits"(Liu et al., 2015).
The term Cloud Computing used in this thesis is defined as" a parallel and distributed
computing environment or service model that enables real-time delivery of products,
services and solutions over the Internet or some centralized access points to the
clients rather than installed locally on the user's device."
This thesis represents clearly the customer‘s point of view rather than technical or
architectural requirements. It is not to say that technical and architectural
requirements are not important, but what the customers pay most attention to are the
benefits they can get from the technology. For example, a real-time delivery of
products and services is more important than whether the products and services are
provided via P2P network, VPN network or direct via Internet.
Given the scope of this thesis, it is impossible to study all kinds of products and
services ―in the Cloud‖, even though the market is still at a very early stage. A
detailed review of the current market situation of Cloud Computing and a layered
structure of different service providers (SPs) in this market will be given in Chapter 3.
In fact, a quite heterogeneous landscape of products and services ―in the Cloud‖
already exists, even for quite a long time: there are services used by normal
consumers every day or many times in a day, for example the E-mail services from
providers like Yahoo, Google or Microsoft: users do not need to use a specific
operation system to get into their mailbox, they do not need to install any specific
client software in their local machines to sending or receiving E-mail, and they can
log into their E-mail account anytime, anywhere, all they need is a web browser and a
Internet connection. The traditional E-mail service is according to this thesis‘s
definition a perfect example of Cloud Computing, but this thesis is giving particular
focus on enterprise customers, which traditionally build and own their data center as a
property, and run and maintain each server and PC separately. Increasingly,
computing addresses collaboration, data sharing, cycle sharing, and other modes of
interaction that involve distributed resources. This trend results in an increasing focus
on the interconnection of systems both within and across enterprises. The emerging
21
Cloud Computing can mean a lot for these enterprises because of its potential in cost
saving and technological advances (Pike et al, 2010).
Like many other emerging technologies, the concept ―Cloud Computing‖ often leads
to confusion about its exact connotation and denotation, because there is no widely
accepted framework to define the concept, and this new technology is still associated
with many other already existing technologies and concepts. For Cloud Computing,
such technologies and concepts include Virtualization, Grid Computing, and Utility
Computing. Chapter 2.1.2, 2.1.3 and 2.1.4 provides a detailed comparison of Cloud
Computing and these computing concepts.
2.2.2 Comparing with Virtualization:
Virtualization was brought out in 1960 when IBM made a logical partition in their
own VM/370 mainframe machines. The idea behind virtualization is to virtualize the
underlying physical hardware or software resources either by software or hardware
tricks. The virtualized environment is called as VM or Guest and the virtualizing
software is referred as virtualization layer or VMM (virtual machine monitor) or
hypervisor. Each VM is a logical existence or imitation of underlying physical
hardware (Host), and it mimics the real characteristics of host that is capable of
running own OS (operating system) (Guest OS).
Virtualization is an abstract concept, and covers the definition of some related to IT
resource integration and management. Virtualization refers to through to the user
blocking these resources IT resources in physical properties and the boundary of a
merger. Specifically, the software and hardware is separate with virtual technology. It
separates the software from them in hardware installation. Can be implemented at
multiple levels of server architecture virtualization, including storage, server,
network, and the application and operating system, realize the effective sharing of
resources. For the upper level resource scheduling and load application provide
uniform and transparent bottom IT resources platform, and provides the dynamic
adjustment and optimization of resource allocation (Liu, 2012).
One of the initial steps toward cloud computing is incorporating virtualization, which
is separating the hardware from the software. In the past, transitions of this magnitude
meant rewriting code, such as the transition from the mainframe to UNIX.
22
Fortunately, the transition to VMware does not require the rewrite of code, and this
has fueled the speed of the move toward virtualization software. There still will be
challenges in this transition but, overall, the consolidation of servers into the virtual
world has been fairly rapid with many applications making a seamless transition. The
journey to get to cloud computing begins with virtualization with the cloud OS
providing infrastructure and application services. The infrastructure services are the
ability to virtualize server, storage, and the network, as well as application services
that provide availability and security for the applications that are being utilized in the
cloud environment. The next step is adding some of the many cloud applications that
include how to do charge-backs and other application software. These cloud-like
capabilities include billing for usage, the ability to do self-service, and many others.
Charging for consumption, even if it is internal, will lead to better management, with
the ability to keep track of what services the consumer is utilizing. In addition, with
cloud computing, there is the ability to program in more self-service by the end user
in order to keep costs down (Kremer, 2013). Virtualization technologies are partition
hardware and thus provide flexible and scalable computing platforms. Virtual
machine techniques, such as VMware2, and Xen3 offer virtualized IT infrastructures
on demand. Virtual network advances, such as Virtual Private Network4 (VPN),
support users with a customized network environment to access Cloud resources.
Virtualization techniques are the bases of the Cloud Computing since they render
flexible and scalable hardware services (Shawish et al, 2014).
The figure bellow shows the ranking of technologies CIO (Chief Information Officer)
selected as one of their top five priorities in 2011.
Figure 2.3 Ranking of technologies CIOs (Gartner, 2011)
23
As it is seen in Figure 2.3 the cloud computing has become the most important
technology for operations coming from nowhere in 2008, to rank 16th in 2009, to 2nd
in 2010, being 1st in 2011.While virtualization has been considered as important for
the last 4 years.
Cloud Computing is not yet the same as virtualization. Firstly, as described before,
virtualization was often used to utilize the usage of a single machine rather than to
build a combined network; that kind of ―single machine virtualization‖ is not really
within the scope of Cloud Computing. Secondly, although virtualization is a useful
tool at the operation system (OS) level to provide hardware portability and OS
segregation, but virtualization in-and-of-itself does not provide necessary capabilities
of Cloud Computing, like scalability, system continuity and certain level of QoS. To
deliver the desired usage of Cloud Computing, virtualization technology should be
used alongside other components of dynamic IT infrastructure. Compared to
virtualization, Cloud Computing is more like a kind of ―technology cluster‖, which
contains more than one distinguishable, but interrelated elements of technology
(Rogers, 2013).
Virtualization is certainly one among these elements, but so do distributed
technology, load balancing technology, and web services, to name just a few. This
kind of bundled innovation package usually leads to greater flexibility in development
process and faster adoption in the market.
A good example of how virtualization and Cloud Computing are tightly connected is
the Citrix XenDesktop, a desktop virtualization system that centralizes and delivers
―desktop as a service‖ to enterprise users anywhere. This virtualization technology
avoids installation of all the different office software on the user‘s local machine and
provides ubiquitous access to the software they need, and in the meantime, the system
update, backup and other maintenance become much easier and more time-efficient.
What the XenDesktop delivers, is a typical Cloud Computing service, although the
services are not necessarily provided via Internet. Another commonly-used
virtualization technology in Cloud Computing is the 3Tera‘s Applogic, which can
eliminate the binding of software to hardware in a Grid/Cloud Computing system.
The Applogic system enables software running in a completely virtualized execution
space with virtualized access to storage and networks.
24
Almost any piece of Linux software can be made into a virtual appliance, which
enjoys a great scalability because it consumes no processing resources and only a
small amount of storage when it is not running, and the resource used by each
appliance in production is only assigned at runtime (3Tera,2010).
2.2.3 Comparing with Grid Computing
Grid Computing is a type of parallel and distributed system that involves the
integrated and collaborative use of resources depending on their availability and
capability to satisfy the demands of researchers requiring large amount of
communication and computation power to execute advanced science and engineering
applications. Precedence constrained parallel applications (workflows) are one of the
typical application models used in scientific and engineering fields requiring large
amount of bandwidth and powerful computational resources (Garg et al,2015).
Figure 2.4 Grid architecture (IEEE 2014 projects, 2014)
A well-known example of grid computing in the public domain is the ongoing SETI
(Search for Extraterrestrial Intelligence) @Home project in which thousands of
people are sharing the unused processor cycles of their PCs in the vast search for
signs of "rational" signals from outer space. According to John Patrick, IBM's vice-
president for Internet strategies, "the next big thing will be grid computing."
A number of corporations, professional groups, university consortiums, and other
groups have developed or are developing frameworks and software for managing grid
computing projects. The European Community is sponsoring a project for a grid for
high-energy physics, earth observation, and biology applications. In the United States,
the National Technology Grid is prototyping a computational grid for infrastructure
25
and an access grid for people. Sun Microsystems offers Grid Engine software.
Described as a DRM (distributed resource management) tool, Grid Engine allows
engineers at companies like Sony and Synopsys to pool the computer cycles on up to
80 workstations at a time (At this scale, grid computing can be seen as a more
extreme case of load balancing).
Grid computing appears to be a promising trend for three reasons: (1) its ability to
make more cost-effective use of a given amount of computer resources, (2) as a way
to solve problems that can't be approached without an enormous amount of computing
power, and (3) because it suggests that the resources of many computers can be
cooperatively and perhaps synergistically harnessed and managed as a collaboration
toward a common objective. In some grid computing systems, the computers may
collaborate rather than being directed by one managing computer. One likely area for
the use of grid computing will be pervasive computing applications - those in which
computers pervade our environment without our necessary awareness.
First, we can compare those from job scheduling of grid computing. Job scheduling is
the core value and aim of grid technology, its aim is to use all kinds of resources. It
can divide a huge task into a lot of independent and no related sub tasks, and then let
every node do the jobs. Even any node fails and doesn‘t return result, it doesn‘t
matter; the whole process will not be affected. Even one node crashes, the task it
should be reassigned to other nodes. Just like grid computing, cloud computing will
make a huge resource pool through grouping all the resources. But the resources
provided by cloud are to complete a special task. For example, a user may apply
resource from the resource pool to deploy its application, not submit its task to grid
and let grid complete it. From this point, the construction of grid is to complete a
specified task, then there will be biology grid, geography grid, national educational
grid and also. Cloud computing is designed to meet general application, and there are
not grid for a special field (Zhang, 2010).
Second, cloud will have effects in three aspects: the application in internet, product
application model and IT product development direction (Kraan and Yuan, 2009).
Of course, this change is not subversion but some new characters that has been added.
This advantage is a challenge to grid technology. When grid come it to being, it has
some advantages, such as: you can provide unlimited compute power through any
26
computer, and can get a great deal of information. This environment can help
enterprise complete tasks that are very hard before, and use their systems efficiently,
to meet the user‘s requirement and decrease the management cost. Cloud computing
extends these advantages. More and more applications will be completed through
Internet by cloud computing. Cloud computing will extend the application of
hardware and software, and will change the application model of hardware and
software. Users can get an application environment or application itself not buying
new servers and new software. To the users, the hardware or the software need not at
his side or only used by himself, it can be available and virtual resources. And
available resources are not limited inside the enterprise, it can be extended hardware
and software attained through Internet. The development direction of IT product will
be changed to meet the above two conditions.
2.2.4 Comparing with Utility Computing
The idea of computing utility was realized as early as 1966, where it was envisioned
that computing networks would mature to reach a point where the idea of 'computer
utilities' was made a reality and worked in similar principle to electrical and telephone
utilities; able to provision computing service such as computing resources,
development platforms or applications to consumers (Leesakul et al, 2014).
A few years later, Leonard Kleinrock, one of the chief scientists of the original
Advanced Research Projects Agency Network (ARPANET) project which was the
initial form of today‘s Internet, brought this concept a step further by saying: ―As of
now, computer networks are still in their infancy. But as they grow up and become
more sophisticated, we will probably see the spread of ‗computer utilities‘ which, like
present electric and telephone utilities, will service individual homes and offices
across the country‖ (Kleinrock, 2011).
And Utility Computing has defined as following: ―Utility Computing has sparked
imaginations with visions of Pay-as-You-Go (PAYG) billing, and dynamic resources
for years. The concept is simple…businesses subscribe to an utility computing service
and pay for the resources they actually use.‖ (3Tera, 2008) And a similar but more
concrete definition can be found by M. A. Rappa from the IBM Global Services
―Utility Computing is the delivery of infrastructure, applications, and business
27
processes in a security-rich, shared, scalable, and standards-based computer
environment over the Internet for a fee. Customers will tap into IT resources - and pay
for them – as easily as they now get their electricity or water‖ (Rapp, 2014). Although
the latter definition hasn‘t literally mentioned ―Pay-as-You-Go‖ (PAYG) model, but
the analogy between Utility Computing and electricity or water indicated clearly the
inherent price model of Utility Computing.
The vision of Internet and especially of the computing utility mentioned before, based
on the service provisioning model (like the electric and telephone utilities), anticipates
the massive transformation of the entire computing industry in the 21th century
whereby computing services will be readily available in today‘s society ( Buyya et al,
2011).
Here we see a major similarity of the concept Utility Computing and Grid
Computing: computing service users need to pay providers only when they access
computing services, and they no longer need to invest heavily or encounter difficulties
in building and maintaining complex IT infrastructure. Cloud Computing shares these
features too, but Cloud Computing is not necessarily built on an entire ―Pay-As-You-
Go‖ basis, and migration cost as well as other problems of Cloud Computing services
do not necessarily lead to an easily built IT infrastructure. In this thesis, Utility
Computing will be seen as part of the whole Cloud Computing concept. For example,
some services provided by Amazon AWS, the current leading Cloud Computing SP,
can be regarded as typical ―utility-like‖ services. Cloud Computing is a broader
concept because it is not just about the basic resources and infrastructure, but about
the application design, deployment and operation too.
2.3 Market Participants in the Cloud Computing Business
In recent years there has been an exponential growth in the number of
vendors offering cloud services with a corresponding increase in the number of
enterprises looking to consume them. Gartner predicts that by 2016, cloud computing
services will form the bulk of new IT spending ( Gartner, 2014).
In this thesis, We define main participants in the Cloud Computing business as either
service providers (SPs) , service buyers/users , service broker or auctioneer.
28
The Cloud service providers provide Cloud services like computational power, data
storage, and software or computer networks. The users have applications or require
different services provided by Cloud resource providers. Each user has a broker who
manages and generates eContract from eContract generator that contains user
requirements, QoS policy and price value for the set of services the user agrees to pay
in the auction and hands payments to Cloud providers.
Figure 2.5 Global Cloud exchange and market infrastructure for trading services. (Bai et al, 2010)
A SP in the market is usually responsible for price setting, admission control and
resource management. Service buyers/users are their counterparts, and as defined, an
organization can be a SP and a service buyer at the same time. Another common type
of market participants is the service broker. Like other markets, Cloud Computing
markets also need intermediates (brokers) to create and maintain relationships with
multiple cloud service providers. It acts as a liaison between cloud services customers
and cloud service providers, selecting the best provider for each customer and
monitoring the services.
In the definition of this thesis, the role of market broker is mainly covered by
providers of platforms for Cloud Computing resource exchange, including raw
computing power and applications. The responsibility of an auctioneer includes
setting the rules of the auction and conducting the combinatorial auction.
29
2.4 Market Structure
In terms of market structure of Cloud Computing, this thesis focuses on the forms of
transaction, i.e. how transactions of Cloud Computing services are coordinated.
Typical forms of market coordination include:
The short-term contract, where service users can buy the desirable service any
time they want, from an open and ubiquitous market, without or almost without
any long-term commitment to the SPs. This indicates the flexibility by decision-
making of both sites as well as the instability of the service contracts.
The in-house transaction, which means the buyers prefer not only to receive the
services, but also to own the whole products and infrastructure, therefore gain the
whole control of the service activity.
The long-term contract, which is a mixture form between short-term contract and
in-house transaction. The long-term contracts are usually based on a certain
framework between the SP and the service buyer, and provide the buyer a
mixture of standard service and specialized facility. The long-term contracts link
sellers and buyers for a long period into a bilateral monopoly in form of a large-
scale partnership (Neuhoff, 2010), which can last as long as many years, and
during which the both sides have strictly defined rights and obligations.
A common example of short-term contract is staying in a hotel: the buyers can choose
any hotel and stay as long as they want, for one day or a month. There are some terms
and conditions between the guest and the hotel, like room cleaning service will be
provided every day from the hotel, and the guest should pay for anything he damaged,
but the guest does not have any long-term commitment to the hotel, i.e. he can move
out of the hotel at any time and simply stop the service. By contrary, an ―in-house‖
solution will be building or buying a property, like a house or an apartment. In that
case, one pays the whole construction cost of the property, i.e. ―buying the product‖;
instead of paying for each night he stays in the house. A third way of finding a place
to stay will be renting a house or an apartment, which is regarded as a typical example
of ―long-term contract‖ here.
31
2.5 Pricing Models
The Pricing mechanism decides how service requests are charged. The price model is
important because pricing is usually one of the biggest influencing factors for a
business decision. Since Clouds are heterogeneous, elastic and scalable, large system
are too complex to be managed centrally. In cloud computing, the complexity of
resources distribution causes that resource owner may take different pricing strategies.
So there are different methods of pricing resources ( Li et al., 2012).
Since cloud services are consumed similar to utility services such as electricity or
water, most providers have applied usage-based pricing with services charged by the
hour or minute, and user payments are tied to actual usage. Users, however, have
shown concern, since it is difficult to calculate total cost.
For the SPs, an inappropriate price model could either lead to excessive reluctance of
potential users to migrate and update to new services, or alternatively, to excess
demand that they cannot fulfill profitably or scale to meet reliably. Either scenario
could be substantially damaging for the development of Cloud Computing.
This thesis derives the ―purchasing cost‖ (i.e. not the transaction cost) of using Cloud
Computing services directly from those price models. There are many different price
models in the business world, and so far, a detailed comparison of different price
models from a market‘s view was not been drawn. Nonetheless, it may become a
critical influencing factor in the consumer's decisions about whether and how they
want to use Cloud Computing services, because one of the most discussed feature of
Cloud Computing is that the users do not need to install the software or applications
in every local machines and can use the software as a service, the so-called SaaS
model.
Naturally, in such business model, users can be charged based on their actual usage of
resources, which is described as the ―Pay-as-You-Go‖ (PAYG) price model.
Interestingly, not every SP in the market chooses the PAYG model by now; instead of
that, the traditional Flat Rate model, as well as a Mixture model, which combines
certain monthly or annually basic charge (Flat Rate) with a PAYG price schedule (for
usage surpassing certain amount) are still very popular. This phenomenon leads to the
discussion in this thesis about what are the influencing factors in choosing different
31
price models for different Cloud Computing services. A comprehensive comparison of
all existing price models is beyond the scope of a master thesis. Therefore, the
following price models are chosen as researching objects for this thesis, simply
because they are by now the most popular models for existing Cloud Computing
services in the markets:
PAYG model: also known as ―usage-based price model‖, by which the users are
charged according to their actual usage of resources. PAYG model provides the
business with a more accurate picture of usage; this enables the business hold
itself accountable when actual consumption does not match the originally planned
usage. Additionally, this information facilitates planning for future consumption;
the business can revise up or down future resource needs. Cloud Computing
services must change its pricing models and billing strategy to make expenditures
more predictable so that the business can budget accordingly. IT must be
thorough, including all the elements that make up the price so that the comparison
with providers is based on equal pricing models. Pricing can include other aspects
that differentiate the service to the business, (e.g., SLA or trust), which may also
be the key objectives to the business. Accurate pricing models also help Cloud
Computing services plan for demand and supply. Accurate resource planning is
the key for Cloud Computing services to ensure sufficient capacity for the
projected demand. If a resource is scarce, prices can be increased to drive demand
down (Galhardi et al. ,2011 ).
Due to the technical obstacles of billing and accounting, PAYG model: (hardware
as well as software) was often discussed, but rarely implemented until recently.
Another problem about the PAYG model is the matching between price and costs:
the software and computing resources are often regarded as typical information
goods, for which the traditional marginal cost pricing method cannot be applied,
since the marginal cost of information goods is zero. However, researchers like K-
W. Huang and A. Sandararajan argued that the On-Demand computing services
are not really information goods, because their provision involves ―non-trivial
variable costs that relate to customer service, billing and monitoring‖ (Huang,
2010).
32
Flat Rate model: users are charged a fixed amount per time unit, irrespective of
actual usage of resources or applications. As the simplest and most convenient
price model for both sides of market participants, Flat Rate model requires no
accurate measurement for billing and accounting, but provides no incentive of
optimizing the resource allocation, because the buyers are insensitive to the actual
cost of their service/resource requests. The problem is that flat rate includes no
price variation information.
Mixture model: a mixture of PAYG & Flat Rate models. Users are charged a
certain fee for resource usage within a certain period, and under a certain cap e.g.
20$ per month for 500 GB online storage space. This fee is fixed no matter the
500 GB storage space is actually used or not. Usage beyond this amount will be
charged based on the actual usage then.
2.6 Homogeneity of Cloud Computing Services
One of the many, many splits within the cloud camp is between homogeneous and
heterogeneous clouds. Simply put, a homogeneous cloud is one where the entire
software stack, from the hypervisor (or remote cloud provider), through various
intermediate management layers, all the way to the end-user portal, is provided by one
vendor. A heterogeneous cloud, on the other hand, integrates components by many
different vendors, either at different levels (a management tool from one vendor
driving a hypervisor from another) or even at the same level (multiple different
hypervisors, all driven by the same management tool) (bmc.com, 2015).
The argument for homogeneous environments is that because everything comes pre-
integrated they are easier to set up, and if something goes wrong there is only one
responsible party – ―one neck to wring‖, as the saying has it. On the other hand, by
giving so much power to one vendor, users place themselves at the mercy of that
vendor‘s commercial and technical strategy
Heterogeneous architectures attempt to bypass this lock-in effect by introducing
components from many different vendors and allocating their use according to a
common set of strategies. At some point, however, a single management component
will need to be introduced. Defenders of homogeneous approaches will counter
charges of lock-in by pointing out that this convergence on a single management layer
33
just moves the lock-in further up the stack, but still leaves users at the mercy of the
provider of that one component.
The false equivalence between platform lock-in and supposed management lock-in is
a neat rhetorical trick, but does not really hold up. Management vendors need to keep
up with the development pace of the managed platforms, or risk falling behind the
competition from other heterogeneous management vendors. Any attempt at predatory
business practices will be nipped in the bud for the same reason (bmc.com, 2015)..
34
Chapter 3
Theoretical groundwork and
frameworks
Chapter Outline: 3.1 Current market overview
3.1.1Genral:
3.1.2 Service provider (including Service intermediate)
3.1.2.1 Pyramid model of Cloud Computing market 3.1.2.2 Service providers in Cloud Computing market:
3.1.3 Service buyer
3.2 Research status
3.2.1 Theoretical groundwork and frameworks for market structure
3.2.1.1 General
3.2.1.2 Public Cloud, Private Cloud, and hybrid model
3.2.1.3The Transaction Cost Theory:
3.2.1.4 Physical asset specificity and service homogeneity
3.2.2 Theoretical groundwork and frameworks for price
model 3.2.2.1 General
3.2.2.2 PAYG, Flat Rate and Mixture Model
3.2.2.3 Service homogeneity and price model
3.2.2.4 Usage frequency and Price model
35
3.1 Current Market Overview
3.1.1Genral:
Cloud computing is an affordable option which creates efficiency and effectiveness,
reduces costs involving electricity, bandwidth, operations and hardware and does not
require functional staff, in-house expertise, space, power and infrastructure.
Furthermore, customers just use and are charged for computing resources they need
since services are delivered on-demand similar to utility providers.
Due to its many benefits, some of which have been outlined, cloud services are
increasingly being embraced by and/or recommended especially for small businesses.
Gartner report provides some insight which says the cloud computing services market
was valued at USD 79.60 billion for the year 2011 grows steeply of 23.21% and reach
a market size of USD 148.9 billion by year 2014. However, with rising competition
and saturation and technology limitations, and grow at a CAGR of 8.39% and reach
USD 205.48 USD by year 2018.
Facing the ever larger demand of Cloud Computing services, various analysis
institutions have mostly made bullish predictions in the market growth of Cloud
Computing in the near future. According to IHS Technology the global business
spending for infrastructure and services related to the cloud will reach an estimated
$174.2 billion 2014, up a hefty 20 percent from $145.2 billion in 2013. And in a sign
of the market‘s vigor, spending will enjoy continued strong growth during the next
few years as enterprises everywhere race to come up with their own cloud-storage
solutions. By 2017, enterprise spending on the cloud will amount to a projected
$235.1 billion, triple the $78.2 billion in 2011, as shown in the figure.
Figure 3.1 Cloud- Related Spending by Businesses to Triple from 2011 to 2017 (IHS, 2014)
36
With the cloud touching nearly every consumer and enterprise around the globe,
spending for cloud-related storage, servers, applications and content will be dedicated
toward building a framework that is rapidly scalable, highly dynamic, available on-
demand and requiring minimal management. As a leading provider of Cloud
Computing service, Amazon Web Services public cloud business as well as
advertising services and co-branded credit card agreements — in the second quarter of
2014 came in at 38.39 percent, based on $1.16 billion in revenue.
3.1.2 Service Provider (including Service Intermediate)
3.1.2.1 Pyramid Model of Cloud Computing Market
Cloud computing services as a whole are certainly not homogeneous, and the
market for Cloud Computing services is not consisting of all similar providers, either.
In fact, services provided in this market are quite different regarding their inherent
characteristics as well as their business models. The figure below demonstrates a
layered structure of current Cloud Computing market (Blau, 2011) and (Youseff et al.,
2008).
Figure 3.2 ―Cloud Pyramid‖: Layered Structure of Cloud Computing Services
a. Cloud Technology Providers:
They are basically the ―Cloud enablers" because it refer to organizations (typically
vendors) who are not cloud providers per se, but make available technology, such as
cloud ware, that enables cloud computing. Vendor that provides technology or service
that enables a client or other vendor to take advantage of cloud computing.
Cloud Platform Providers
Cloud Infrastructure/Physical
Resources Providers
Cloud Technology Providers
37
The Technology Providers on the current market can be divided into two types:
a) Companies developing and implementing Cloud Computing technology by
themselves as Amazon, by 2005, Amazon had spent over a decade and millions of
dollars to design and implement a whole new, idiosyncratic structure for its ecosystem
of Cloud Computing services. Amazon launched Amazon Web Services (AWS) so
that other organizations could benefit from Amazon‘s experience and investment in
running a large-scale distributed, transactional IT infrastructure. AWS has been
operating since 2006, and today serves hundreds of thousands of customers
worldwide. Today Amazon.com runs a global web platform serving millions of
customers and managing billions of dollars‘ worth of commerce every year.
b) Companies focusing purely on technology and delivering the technology to other
Cloud SPs as 3tera which is among the pioneers in the cloud computing space, having
launched its AppLogic system in February, 2006. Cloud computing is the set of
technologies and business practices that enable companies of all sizes to build,
deploy, monitor and scale applications using resources accessed over the internet.
Web 2.0, SaaS, Enterprise and government users are adopting cloud computing
because it eliminates capital investment in hardware and facilities as well as reduces
operations labor.
b. Cloud Infrastructure/Physical Resources Providers:
Providers of Cloud Infrastructure provide the consumer provision processing, storage,
networks, and other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating systems and
applications. The consumer does not manage or control the underlying cloud
infrastructure but has control over operating systems, storage, and deployed
applications; and possibly limited control of select networking components (e.g., host
firewalls). (Badger et al, 2012).
The physical resources in Cloud Computing market can be categorized into three
categories: a) Computational resources, which are commonly calculated in CPU
hours. Typical examples are the Amazon EC2 and Google App Engine; b) Data
storage; and c) Communication (Youseff et al, 2008). Among all Cloud Computing
services, providing data storage service is relatively easier compared to others,
because the physical storage devices are already commodities and the virtualization
38
technology for storage system is already mature. Therefore, the number of mid-sized
providers of Cloud storage services is growing fast. Typical examples include Areti,
Enki, Terremark etc., as well as some traditional data storage/ data center providers
like EMC, AT&T etc.
c. Cloud Platform Providers:
Platform Provider offers computing resources in form of VMs, which have various
resource capacities with corresponding charges. (Qiao et al., 2012) These resource
capacities typically are including operating system, programming language execution
environment, database, and web server. Application developers can develop and run
their software solutions on a cloud platform without the cost and complexity of
buying and managing the underlying hardware and software layers.There are basically
two types of Cloud platforms:
a) Platform as a software environment for developing, testing, deploying and running
Cloud Computing applications. A known example of this type is Google‘s App
Engine, which provides developers a Phyton runtime environment and specified APIs
to develop applications for Google‘s cloud environment. Another example is
Salesforce‘s AppExchange platform5 that allows developers to extend the Salesforce
CRM solution or even develop entire new applications that runs on their cloud
environment.
b) Platform for raw computer resources exchange, A known example of this type is
the Ebay for computer resources, can only be built in an environment where exchange
of raw computer resources is already a common business, and the widely expected
standards for the exchange already exist. As these conditions are not yet reached in
the market, the only currently available platform for computer resource exchange is
the Zimory Marketplace from Zimory GmbH, a spin-off of Deutsche Telekom
Laboratories.
d. Cloud Application Providers:
The cloud application providers are the most visible providers to the end customer.
The application layer usually accessed through web-portals and thus builds the front-
end, the user interacts with when using cloud services. A Service in the application
layer may consist of a mesh of various other cloud services, but appears as a single
service to the end-customer. Therefore it is the most complex, but also indispensable
39
part of a whole Cloud Computing structure. Examples for applications in this layer are
numerous, but the most prominent might be Salesforce‘s Customer Relationships
Management (CRM) system2 or Google‘s Apps, which include word-processing,
spreadsheet and calendaring.
Cloud applications can be categorized into:
a) elementary applications
b) complex applications
The difference between elementary and complex applications is mainly characterized
by the homogeneity of applications rather than the complexity of their functions. The
reason is: homogeneous applications are more like commodities; hence their
economic characters share more similarity with the basic services in the Cloud
Computing structure, i.e. providing the raw computer resources. And as will be
discussed in more details in Chapter 3.2.1 and Chapter 3.2.2, the main purpose of this
thesis is to examine the possible connection between service homogeneity, market
structure, and price model for Cloud Computing services. Rather than to define which
applications are elementary or complex, this thesis will make classifications directly
based on the results from the customer survey, which will be presented in Chapter 5.
3.1.2.2 Service Providers in Cloud Computing Market:
Though the actual history of cloud computing is not that old (the first business and
consumer cloud computing services websites – salesforce.com and Google, were
launched in 1999), its story is tied directly to the development of the Internet and
business technology, since cloud computing is the solution to the problem of how the
Internet can help improve business technology.
Amazon.com introduced Amazon Web Services in 2002. This gave users the ability to
store data and put a gigantic number of humans to work on very small tasks (such as
Mechanical Turk), amongst other services. Facebook was founded in 2004,
revolutionizing the way users communicate and the way they store their own data
(their photos and video), inadvertently making the cloud a personal service.
In 2006, Amazon expanded its cloud services. First was its Elastic Compute cloud
(EC2), which allowed people to access computers and run their own applications on
them, all on the cloud. Then they brought out Simple Storage Service (S3). This
41
introduced the pay-as-you-go model to both users and the industry as a whole, and it
has basically become standard practice now.
The PaaS (platform as a service) let companies‘ developers build, store and run all of
the apps and websites they needed to run their business in the cloud. Google Apps
launched in 2009, allowing people to create and store documents entirely in the cloud.
Most recently, cloud computing companies have been thinking about how they can
make their products even more integrated. In 2010 Salesforce.com introduced the
cloud-based database at Database.com for developers, marking the development of
could computing services that can be used on any device, run on any platform and
written in any programming language.
On March 1, 2011, IBM announced the IBM SmartCloud framework to support
Smarter Planet.[22]
Among the various components of the Smarter Computing
foundation, cloud computing is a critical piece.
On June 7, 2012, Oracle announced the Oracle Cloud. While aspects of the Oracle
Cloud are still in development, this cloud offering is posed to be the first to provide
users with access to an integrated set of IT solutions, including the Applications
(SaaS), Platform (PaaS), and Infrastructure (IaaS) layers.
In 2013, Akamia has a network of over 100,000 servers deployed in more than 90
countries. These servers reside in more than 1,000 of the world's networks gathering
real time information about traffic, congestion, and trouble spots. Each Akamai server
is equipped with proprietary software that uses complex algorithms to process
requests from nearby users, and then serve the requested content.
February 2014, RightScale conducted its third annual State of the Cloud Survey,
asking 1,068 technical professionals across a broad cross-section of organizations
about their adoption of cloud computing. Twenty-four percent of respondents came
from larger enterprises, representing organizations with more than 1,000 employees.
This year‘s survey on cloud computing trends found that public cloud adoption is
nearing 90 percent on the journey to hybrid cloud as enterprises seek to expand their
portfolio of cloud services.
41
But as more and more companies see the potential of the Cloud Computing markets,
both traditional IT companies like IBM, and new technical startups begin to expand in
this new market, and Cloud Computing services are becoming more important than
just a way to cover expenditures caused by under-utilized infrastructure.
Below is a list of the 38 most active SPs in current Cloud Computing market.
Although the market is still at its early age, listing all the SPs in the market will be far
beyond the scope of a master thesis. Therefore, this list of selected SPs is mainly
based on the company‘s influence, the kinds of services they provide.
Table 3.1 the 38 most active SPs in current Cloud Computing market.
The above table indicates following facts:
1. The Cloud computing market is expanding quickly: while many projects or
startups are still in beta or preview release, more and more companies, especially
the ―traditional players‖ in IT services like Dell, IBM, Microsoft and SUN are
providing formal release of their Cloud Computing services. Just during the past
two months from end 2014 to Feb. 2015, Amazon AWS has added new services
(Amazon WorkMail) into their ecosystem of Cloud Computing and Amazon EC2
No.
Companies Active/
Beta
A/P/R
/T
No. Companies Active
/ Beta
A/P/R/
T 1 10Gen B P, A 20 Eucalyptus A T
2 37signals A A 21 FlexiScale
(Xcalibre)
A R
3 3Tera A R, T 22 Fortress ITX A R
4 Adobe
Acrobat
B A 23 Gh.o.st B A
5 Akamai A A, T 24 GoGrid/
ServePath
B R
6 Amazon
AWS
R 25 Google A R, P
7 Aptana B R, P 26 IBM A A, T
8 Areti
(Alentus)
A R 27 Joyent A R, A
9 AT&T A R 28 Microsoft
(Azure
platform etc.)
A R, A, P
10 Cassatt A A, 29 Mosso A P
11 Cisco
Systems
A A, T,
P
30 NetSuite A A
12 Citrix (inc.
XenSource)
A A, T 31 Project
Caroline
(SUN)
B P
13 Cloudwork
s
A R, A 32 QuickBase A P, A
14 cohesiveFT A P, T 33 Right Scale A A, T
15 Dell A R, T 34 Salesforce
A P, A
16 Elastra A R, P,
T
35 SUN
Network.com
A R, A
17 EMC (inc.
VMware &
Mozy)
A R, T,
A
36 Terremark A R
18 Enki A R 37 Workday A A
19 Enomaly B T 38 Zoho A P, A
42
Container Service is now available in the US West (Oregon) region. Many other
companies in the Cloud Computing market have experienced the same or even
higher speed of expansion.
2. Many companies are trying to open up more than one market segment: in the early
stage of market development, a mature market structure is not yet available, and
companies are often forced to provide ―bundle‖ of resources and services, because
there are no other partners in the market who can provide those resources or
services for them. So as Google or Salesforce wanted to build a platform for sale
and exchange of On-Demand software, they had to use their own computing
resources to deploy them; and as IBM or EMC wanted to sell their new Cloud
Computing applications to attract more data center customers, it must develop
their own technology to support them. Besides, companies are also not sure about
how each market segment will develop, and which segment is the potential best fit
for them. An example of companies changing their service catalog is the
Network.com from SUN. When this service was announced back to 2004, it was
highlighted by SUN as a Utility Computing service for enterprise customers, but
after being proofed unattractive for the massive business use, SUN is conducting a
transition of the Network.com now, preparing to provide a more mature service
combining the basic computing resources with useful applications. This example
shows that at the infancy stage of a technical trend, the best strategy for the SPs in
the market, especially the big ones with more resources, may be ―try-and-fail‖:
opening up more market segments parallel, and then focusing on those with the
most success.
3. Traditional IT service companies and startups are following different routes of
development: companies like Dell, IBM and EMC are trying to provide Cloud
Computing services as ―add-on‖ or additional service. This is because they regard
Cloud Computing as a technology in its early age, and thus are not eager to put it
into mass use; in the meantime, this also helps them to introduce Cloud
Computing services to their existing, but more innovative customers, even makes
the research and test of services easier by targeting a small scope of ―pioneer‖
customers. By contrast, startups are usually focusing more on the most innovative
services, like Utility Computing and SaaS. This is partly because the traditional
43
players in these fields, like Seagate, the leading storage device provider, or SAP,
the leading ERP system provider, are not yet very active in putting their products
or services ―into Cloud‖
4. Open source projects are playing an important role in the Cloud Computing
market: there is no wonder that Cloud Computing services are welcomed by
various open source projects, since they have the potential in lowering costs,
especially initial investments of the projects, and surpassing the barriers for
software development too. In the meantime, open source projects help to enrich
the services provided in the Cloud Computing market or a Cloud Computing
ecosystem, e.g. the Eucalyptus, imitates the experience of using Amazon EC2, but
give the users the possibility of choosing computing resources by themselves,
which means they can run the Cloud Computing service internally too.
3.1.3 Service Buyer
I think many of the discussions of cloud computing focus too much on the
implementation side and not enough on who the potential users are and what will be
their needs. Many users don‘t have or need a very precise definition of ―cloud
computing.‖ Indeed, I think that for many people it simply matters whether their
applications and data live on their machines or devices, or if they are run through a
browser or reside somewhere out on the network, respectively. Here are some
possible users for cloud computing.
a. A user of a virtualized desktop on a thin or fat client: This type of user could
employ software such as Virtual Bridges VERDE server to run desktop
applications on a powerful server somewhere, but have the screen output delivered
down to a local device such as a netbook, laptop, or desktop machine. While today
many people speak of virtualized Linux desktops, we can imagine a future where
many organizations run native local Linux desktops and then virtualize down from
the cloud a Windows desktop for only light and occasional use of applications that
have not yet been ported or replaced.
b. A non-technical end user who accesses services through a browser or via
applications such as disk backup to remote storage: This is a very broad view of
44
how a user might use the ―cloud.‖ Here he or she would have a sense that instead
of running a local application, software like a word processor or CRM front-end is
used in a browser like Firefox. These cloud-based applications are helping to
reduce users‘ dependencies on working on any particular operating system, and
therefore allowing more and more use of Linux and Mac/OS X in businesses and
organizations. On the other hand, traditional desktops can be extended to use the
cloud for remote storage. For example, I use Jungle Disk to automatically backup
certain folders and files nightly to Amazon S3. (They also support Rackspace
Cloud Files, which helps make my point.)
c. A “cloud choreographer” who strings together cloud-based services to implement
business processes: Here I‘m borrowing the notion of choreography from web
services or SOA (Service Oriented Architecture). The idea is that new applications
are constructed from program logic and across-the-network calls into cloud
services. It starts to get interesting when more than one cloud is used, and
therefore further emphasizes the need for open standards and cloud
interoperability. Security issues are always important, but privacy ones strongly
enter into this scenario because of the possibility of improperly sharing
information across services and clouds. This case most clearly shows where SaaS
might be subsumed into the general notion of cloud computing.
d. A service provider who needs to handle peak load demand: A service provider
wants to have the right level of software and hardware resources to provide an
acceptable quality of service to his or her customers. Cloud computing can help
deliver this by allowing the service provider to purchase and configure datacenter
resources for average use, and then use processors or storage from the cloud to
handle spikes.
e. A developer who employs dynamic resource allocation in clouds to speed
application or solution creation: While a software developer might spend a lot of
time thinking and working in an integrated development environment where the
need for computer resources is small, other activities such as compiling, linking,
and testing may be very computer resource intensive. For those times, a private or
public cloud could be used so that local capital expense for servers can be
minimized. It's very important to observe and measure how and when developers
45
use computing resources before contracting for cloud services. For example, does
an entire workgroup of developers need the resources at the same time, or do the
individuals need fairly randomly? In international efforts such as computer
animations, can one shift of developers use resources no longer needed by others
in a different time zone?
f. An IT system administrator who does not build clouds but deploys onto them,
probably in addition to traditional managed systems: This is the lowest (―closest
to the metal‖) level of user who uses clouds but does not build them. Someone
else configures the datacenter but it is this admin‘s job to decide how to best
deploy applications onto either traditional dedicated servers or shared cloud
servers. He or she would need to understand the resource needs of the
applications, as well as the security parameters.
Each customer segment will move to the cloud in different ways. While
Transformational customers have the highest adoption rates today, Heterogeneous
customers will nearly match them within three years. Safety-conscious customers will
adopt more slowly, but at twice the size, this segment will cause a significant increase
in spending growth. For Price-conscious customers, adoption will nearly quadruple as
prices come down. Finally, Slow and Steady customers, who have barely begun to
experiment, will see meaningful adoption over the next three years. The segment
represents a sizable opportunity.
As mentioned in Chapter 2.1.1, this thesis is focusing on the enterprise customers
rather than the individuals consumers. Currently, the customers of Cloud Computing
are mainly small companies and startups.
46
3.2 Research Status
3.2.1 Theoretical Groundwork and Frameworks for Market Structure
3.2.1.1 General
As mentioned in Chapter 2.3, transaction forms of Cloud Computing services are
categorized into three different types in this thesis: the short-term contract, the long-
term contract and the in-house transaction. The short-term contracts of Cloud
Computing services are also regarded as ―Public Cloud‖, because they can be directly
gained from the open market; the in-house transactions are regarded as ―Private
Cloud‖, because they are usually not publicly accessible, and in between of them, the
long-term contracts can be seen as a hybrid model sharing characteristics from both
sides. These different kinds of market coordination forms are assigned different
names from various researchers and many others described the short-term contracts as
―markets‖, in-house transaction as ―hierarchies‖ and the long-term contracts between
them as ―networks‖.
In this thesis, we also use the term ―market structure‖ to describe these transaction
forms.
3.2.1.2 Public Cloud, Private Cloud, and Hybrid model
There are three different models for using cloud computing. These deployment
models may have different derivatives which may address different specific needs or
situations (Amrhein et al., 2010, CSA, 2009). The basic deployment models are
public cloud, private cloud, community cloud, and hybrid cloud (CSA, 2009, Dustin
Amrhein et al., 2010, Grance, 2010).
a. Public Cloud: The first model is public cloud witch allowing users to access
through the web browser interface. Users such as municipal utility bill instead use
time fee to pay. This feature helps to the operating costs of IT declined, however,
in terms of security in public clouds compared to the other models are more
vulnerable to attacks and abuse are one of the ways to prevent incidents of
security controls on both the client and a provider of cloud. It should be noted that
both sides need to identify the scope and authority with their operational
constraints (Jadeja et al., 2012).
47
Figure 3.4: Public Cloud (Amrhein et al., 2010).
b. Private Cloud: The second model is the private cloud. A private cloud data center
operation within an organization is carried out. The advantage to manage the
maintenance, security, update, improve and control the development and application
have been considered. Resources and programs are managed by the organization itself.
This type of cloud security is improved because only members of the organization are
allowed to use cloud services (Jadeja et al., 2012). This type of cloud for organizations
with large area and can be managed by a third party.
Figure 3.5: Private Cloud (Amrhein et al., 2010)
c. Hybrid Cloud: The third model is a hybrid cloud is a combination of public and
private cloud group. In this model, a private cloud is connected to one or more
external cloud service. The main activities that lead to a competitive advantage
48
for them to be done by the private cloud side, whereas the activities of other
clouds (public or associative) are complete (Jeyd et al., 2014).
Figure 3.6: Hybrid Cloud (Amrhein et al., 2010)
The Public Cloud, such as Amazon EC2, Google App Engine, or Zimory.com, is the
broadly accepted form of Cloud Computing, and is usually associated with other
terms like Software-as-a-Service (SaaS) and Utility Computing. On the contrary, the
term ―Private Cloud‖ can be controversial for people believing that a Cloud
Computing service must be delivered via Internet, which is not necessarily the case.
The Internet is the largest, truly global-scale ―Cloud‖, but besides that, plenty of
smaller ―Cloud‖ can be built at organizational or enterprise level, which enable the
sharing of computer resources for members of different projects or departments
within the organization. Most cloud vendors let you come and go as you please. The
minimum order through XCalibre‘s FlexiScale cloud, for example, is one hour, with
no sign-up fee. Amazon EC2‘s policy is equally as lenient. This makes clouds an ideal
place to prototype a new service, conduct test and development, or run a limited-time
campaign without IT resource commitments ( Staten,2008). While the description of
services provided by FlexiScale and Amazon EC2 is true, there is also a noticeable
number of SPs, such as IBM and Dell, which are providing more complex Cloud
Computing services in the market. These services can only be delivered in a
customized manner and therefore bundled with long-term contracts. Back to the
definition of Cloud Computing in Chapter 2.1.1, it is clear that this thesis will not
restrict Cloud Computing services in a short-term framework.
49
The following table gives a brief comparison for the three market structures:
Table 3.2 Comparison of Public Cloud, Private Cloud and Hybrid Model
Public Cloud Hybrid Model Private Cloud
Deployment location External External Internal
Service delivery via Internet Internet Internal networks
(LAN, VPN etc.) Initial investment Low Medium High
Ex-ante contracting No Yes Yes
Long-term commitment
No Yes Yes
SLA guarantees complex & hard to achieve Easy to achieve Easy to achieve
Service provider (SP) Startups Traditional SPs Both
Choosing between Public or Private Cloud services can be important for users in
terms of the different models of service delivery, contracting and pricing.
A report from showed that one of the biggest advantages of the Private Cloud over
Public Cloud is that users can directly connect to the Cloud services via a VPN
network rather than Internet, which greatly increase the speed and stability of
applications.
As for this thesis, the focus of study is on the cost side, therefore, it is interesting to
examine whether the Transaction Cost Theory can provide a useful framework to
explain the constellation of those different market structures, i.e. Public Cloud,
Private Cloud and Hybrid Model. The short-term contracts are adopted by the
majority of SPs; where we have less clarity is, whether the short-term contracts are
still the dominant transaction form if ranked by contract volume instead of the number
of SPs, because the traditional IT SPs, like Dell, IBM and EMC, are all in favor of the
other two forms, and their contract volumes are usually much bigger than those of the
startups. A comparison of these transaction forms by contract volume may shed more
light on the current market constellation, but is beyond the scope of this thesis.
Please note that the Public Cloud, Private Cloud or Hybrid Model discussed here are
all transaction-based, not entity-based. A company as an entity can purchase Cloud
Computing services in different forms simultaneously, or even use more than one
form from these three for a same service.
51
3.2.1.3The Transaction Cost Theory:
The term "transaction cost" is frequently thought to have been coined by Ronald
Coase, who used it to develop a theoretical framework for predicting when certain
economic tasks would be performed by firms, and when they would be performed
on the market. However, the term is actually absent from his early work up to the
1970s. While he did not coin the specific term, Coase indeed discussed "costs of
using the price mechanism" in his 1937 paper The Nature of the Firm, where he
first discusses the concept of transaction costs, and refers to the "Costs of Market
Transactions" in his seminal work, The Problem of Social Cost (1960). The term
"Transaction Costs" itself can instead be traced back to the monetary economics
literature of the 1950s, and does not appear to have been consciously 'coined' by
any particular individual (Kissell et al., 2003).
Arguably, transaction cost reasoning became most widely known through Oliver E.
Williamson's Transaction Cost Economics. Today, transaction cost economics is used
to explain a number of different behaviors. Often this involves considering as
"transactions" not only the obvious cases of buying and selling, but also day-to-day
emotional interactions, informal gift exchanges, etc. Oliver E. Williamson was
awarded the 2009 Nobel Memorial Prize in Economics (Nygaard and Dahlstrom, 2010).
According to Williamson‘s theory, transaction costs are largely influenced by the
following three parameters:
Asset specificity: an investment conducted by a party of the transaction can either
be nonspecific, or idiosyncratic, depending on whether this investment can only
be used for the specific transaction or not. The asset specificity defined by
Williamson is ―the degree to which durable investments that are undertaken in
support of particular transaction, the transaction-specific skills and assets that are
utilized in the production processes and provision of services for particular
customers‖ (Williamson,1985).
Williamson classified asset specificity into four types:
a. Human asset specificity, in those employment relationships which embedded
―learning-by-doing‖ processes.
b. Physical asset specificity.
51
c. Site specificity, by investments with great setup and/or relocation costs.
d. Dedicated assets, which are usually purchased or produced on special
requirements of certain clients, i.e. expanding existing plant on behalf of a
particular buyer.
Uncertainty: refers to the cost associated with explaining and understanding
products. A higher uncertainty means either that the probability distribution of
disturbances remains unchanged but more numerous disturbances occur, or that
disturbances become more consequential (Williamson, 1991).
Frequency of transaction: whether the transactions are occasional or recurrent.
One-time transaction belongs to ―occasional transactions‖ too, as suggested by
Williamson, because they have little difference in terms of participants‘ behaviors
and economic features (Williamson, 1979).
The Transaction Cost Theory is the first organizational theory emphasizing the
importance of asset specificity. And among all the influencing factors/dimensions,
asset specificity is regarded as the most important for the transaction cost analysis
According to Williamson, a higher asset-specificity of investments leads to more
hierarchical contract structures, as opposed to market exchange. This relationship was
already confirmed by many researchers for various industries.
The Transaction Cost Theory was used by researchers to explain the emergence of
electronic markets too. It is obvious that electronic markets advance the physical
markets in terms of search cost and many other concrete transaction costs, but beyond
that, the original purpose of Transaction Cost Theory was trying to explain the
difference between organizations, a more fundamental difference than pure cost
effect. That is why it seems interesting to compare the theory from Williamson with
the reality in the Cloud Computing market: according to the 3-dimensional model
from Williamson, the choice of market structure by the consumers should be strongly
influenced by the factor specificity of various Cloud Computing services too.
The relationship between asset specificity and choice of market structure is one of the
most important hypotheses this thesis is trying to verify for the Cloud Computing
services market, based on the customer survey described in more details in Chapter 4
The transaction costs can be categorized into two types:
52
Ex ante transaction costs: According to Williamson, the ex-ante transaction costs
are ―the costs of drafting, negotiating, and safeguarding an agreement‖
(Williamson, 1985), i.e. the costs such as advertisement, inviting bids from
interested parties and so on. For Cloud Computing services/applications, such as a
specialized simulation software for a financial institution, these costs by open
market transaction can be very high, because the services provided there are
usually standardized, not individually customized (―nonstandard contracting‖); if
the users aim to hold the property of the software, the negotiating process will
usually become much easier, because the customization cost can be easily covered
by the purchasing cost of the users then. For standardized services, the open
market is associated with less ex ante transaction costs because the service can
easily be defined with a few parameters and structures, and the effect of
economies of scale can be highly noticeable.
Ex post transaction costs: ex post costs take several forms and mainly caused by
contract misalignments (Williamson, 1985). For Cloud Computing services such
as Amazon EC2, the typical ex post transaction cost is the business loss of service
users caused by the Amazon‘s system outage. Again, for highly special services
traded in open market, the chance of finding a substitute service in such situation
is very small, hence the potential loss, i.e. the ―switching cost‖, is considerably
high; but for standardized services, the substitute or compensation methods can be
defined in a form of SLA with little difficulty.
In a reduced-form analysis, Williamson concluded that with nonspecific investments,
market participants will choose open market as the main form of transaction; with
highly idiosyncratic investments, they will choose hierarchy, i.e. the ―firm‖; and with
―mixed‖ investments between nonspecific and idiosyncratic, they will choose a hybrid
model between open market an hierarchy, i.e. long-term contracts as the form of
transactions (Williamson, 1991). Based on the assertion of Williamson, users should
prefer Public Cloud for services with low factor specificity and Private Cloud for
services with high factor specificity.
In a more complex analysis considering both asset specificity and frequency as the
influencing factors for the optimal market structure, Williamson categorized the
market structures into 4 types:
53
a. The ―market governance‖, which is equal to short-term contracts in the open
market.
b. The ―trilateral governance‖, which involves no long-term commitment from
either sides of transaction, but assistance from a third party.
c. The ―bilateral governance‖, which is equal to the long-term contracts.
d. The ―unified governance‖, i.e. ―internal organization‖, which equals to the in-
house transactions.
According to the characteristics of these 4 market structures, Williamson drew a
matrix with asset specificity and frequency as two dimensions:
Table 3.3 Matching Market Structures with Asset Specificity and Frequency (Williamson, 1979)
Asset Specificity
Nonspecific Mixed Idiosyncratic
Fre
qu
ency
Occ
asi
on
al
Short-term Contracts
(Market Governance)
Short-term Contracts
(Trilateral Governance)
Rec
urr
ent
Long-term Contracts
(Bilateral Governance)
In-house Transaction
(Unified Governance)
In other words, we can re-formalize the assertion of Williamson as following:
For transactions with high frequency, the optimal market structure is determined
by the degree of asset specificity. And for both mixed and idiosyncratic
investments, the ideal transaction form should be ―transaction-specific‖.
For transactions with low frequency, both parties always prefer the short-term
contracts, no matter how specific the involved investments are. As argued by
Williamson for one-time or very infrequent service, the contracting costs
involving long-term commitments are always too high for the market participants.
54
Therefore, the short-term contracts are consistently the preferred transaction form;
the only question is, whether the both sides conduct the transaction directly, or via
some market intermediate (―trilateral governance‖).
3.2.1.4 Physical Asset Specificity and Service Homogeneity
As mentioned in the Chapter 3.2.1.3, asset specificity has many different forms and
sources. One kind of asset specificity is associated with the physical investments, like
a special machine for certain products, or even a plant. This type of asset specificity
was described by Williamson as ―physical asset specificity‖. The form of asset
specificity is an important factor by shaping the bilateral contracting behaviors, and
plays, along with other forms of asset specificity, a central role in the Transaction
Cost Theory. Physical asset specificity in service industry is directly determined by
how homogeneous the service is. Illustrating an example, where all applications
requiring computing resources are running on a single platform (operating system),
either Unix, Linux, or Windows, the providers of computing resources have no need
to invest in the development of interoperable environment then; this feature of service
reduces the physical asset specificity and the costs, both the service providers and
service users (e.g. the application developers) can easily shift their existing
investments into other market or market segments because of the inherent
interoperability of a single platform.
3.2.2 Theoretical Groundwork and Frameworks for Price Model
3.2.2.1 General
Pricing is the process of determining what a service provider will receive from an end
user in exchange for their services. The cloud computing success in the IT market can
be obtained only by developing adequate pricing techniques. The pricing process can
be as follows: fixed, in which the customer is charged the same amount all the time;
dynamic, in which the price charged changes dynamically; or market-dependent, in
which the customer is charged based on the real-time market conditions. Fixed pricing
mechanisms include the pay-per-use model, in which the customers pay for the
amount they consume of a product or the amount of time they use a certain service.
Subscription is another type of fixed pricing, in which the customer pays a fixed
55
amount of money to use the service for longer periods at any convenient time or
amount. A list price is another form of fixed pricing, in which a fixed price is found in
a catalog or a list. On the other hand, differential or dynamic pricing implies that the
price changes dynamically according to the service features, customer characteristics,
amount of purchased volumes, or customer preferences. Market-dependent pricing,
however, depends on the real-time market conditions such as bargaining, auctioning,
demand behavior, and yield management. The following are the most pertinent factors
that influence pricing in cloud computing (Sharma et al, 2012):
a. Initial costs: This is the amount of money that the service provider spends annually
to buy resources.
b. Lease period: This is the period in which the customer will lease resources from
the service provider. Service providers usually offer lower unit prices for longer
subscription periods.
c. QoS: This is the set of technologies and techniques offered by the service provider
to enhance the user experience in the cloud, such as data privacy and resource
availability. The better QoS offered, the higher the price will be.
d. Age of resource:. This is the age of the resources employed by the service
provider. The older the resources are, the lower the price charged will be. This is
because resources can sustain wear over time, which reduces their financial value.
e. Cost of maintenance: This is the amount of money that the service provider spends
on maintaining and securing the cloud annually.
The main influences on pricing are supply and demand. Demand refers to the level at
which a service or good is desired by customers. The law of demand states that, when
the price of a good or service is higher, fewer customers will demand that good or
service. Supply, on the other hand, reflects the amount of goods or services that the
market can produce for a certain price. Therefore, price is considered a reflection of
supply and demand. As mentioned before, this thesis is focusing on market
acceptance of Cloud Computing services by studying the current and potential
customers (demand) of these services. One of the most important factors determining
whether many customers are willing to use the Cloud Computing services is the price,
and it is not only about how high the price is, but also about what the price model is.
The thesis herein uses the term ―purchasing cost‖ for production costs and price, as in
the view of service buyers. The most direct way to determine the cost is investigating
56
the pricing mechanism of services, because the purchasing cost of a service is simply
determined by the price for each unit of service (which can be measured by use time,
connection time, volume, transaction etc.) and the consumed units.
A customer will evaluate a prospective service provider based on three main
parameters: pricing approach, QoS, and utilization period, in this interest with the
pricing, which is approach describes the process by which the price is determined.
The pricing approach could be one of the following: fixed priced regardless of
volume, fixed price plus per-unit rate, assured purchase volume plus per-unit price
rate, per-unit rate with a ceiling, and per-unit price. (Iveroth et al, 2012) The fixed
price regardless of volume charges the customer a fixed price regardless of the
volume of the service or product utilized. The fixed price plus per-unit charges the
customer a fixed price plus a unit rate. In the assured purchase volume plus per-unit
price rate, the customer pays a fixed price for a certain quantity. If the customer‘s
utilization exceeds that quantity, the customer has to pay a fixed rate per unit for the
extra utilization. In the per-unit rate with a ceiling approach, the customer pays the
per-unit rate up to a certain limit. The provider will not charge the customer above
that limit. In the price per unit approach, the customer is charged a different price per
unit.
It is clear that a technically (or theoretically) highly efficient (and often complex)
price model does not necessarily gain popularity in the real business world, this thesis
intends to accomplish a more detailed study on the commonly existing price models
including Flat Rate pricing, PAYG pricing and a mixture of these two models, instead
of proposing some new price models.
3.2.2.2 PAYG, Flat Rate and Mixture Model
In cloud computing, the complexity of resources distribution causes that resource
owner may take different pricing strategies. So there are different models of pricing
resources. However, the most common models employed in cloud computing are the
pay-as-you go model, Flat Rate model and the Mixture Model.
Pay-as-you go model, customers pay a fixed price per unit of use. Amazon
considered the market leader in cloud computing, utilizes such a model by
charging a fixed price for each hour of virtual machine usage. The ―pay-as-you-
go‖ model is also implemented by other leading enterprises such as Google App
57
Engine and Windows Azure .Another common scheme employed by these leading
enterprises is the ―pay for resources‖ model. A customer pays for the amount of
bandwidth or storage utilized.
Flat Rate model, where a customer pays in advance for the services he is going to
receive for a pre-defined period of time, is also common.
Mixture Model, mixture of PAYG & Flat Rate models, where Users are charged a
certain fee for resource usage within a certain period, and under a certain cap.
Table 3.4 Classification of different payment structures
Flat Rate PAYG Mixture
One-Time Purchase x
Periodical Payment x
Subscription-based Payment
(Software) x
Usage-based Payment
(Hardware)
x
Periodical Fee with Payment
for Extra Use
(Hardware)
X
The table below compares pricing models inclusively in terms of fairness, pros, and
cons. Pricing models fall into two main types: static and dynamic. In static pricing
models, the price remains unchanged after it has been determined. In dynamic pricing,
the price changes dynamically according to factors such as the resources required,
demand, and more.
Table 3.5 Pricing Model Comparison (Al-Ebrahim et al, 2013)
Pricing
model
Pricing Approach Fairness Pros Cons
Pay-as-
you-go
model
Price is set by the
service provider and
remains constant
(static)
Unfair to the customer
because he might pay
for more time than
needed
Customer is aware of
the exact price to be
paid
Resources are reserved
for the customer for the
paid period of time
Service provider might
reserve the resources for
longer than the
customer‘s utilized
Service provider cannot
raise the price when
demand is high; when
demand is low, the user
pays higher than the
market price
Flat Rate
model
Price is based on the
period of subscription
(static)
Customer might
sometimes overpay or
underpay
Customer might
underpay for the
resources reserved if he
uses them extensively
Customer might overpay
for the resources
reserved if he does not
use them extensively
58
Mixture
Model
Price changed
according to the job
queue wait times
(static/dynamic)
Fair to customers
because of the price
authority entity, which
dynamically adjusts
prices within static
limits
Simple and has low
computational
overhead
Must reach an agreement
on common base prices
and variation limits
3.2.2.3 Service Homogeneity and Price Model
Clouds computing must provide a good pricing model that is beneficial for both
parties. It is sometimes hard to find a balance in which both sides agree with the price
set. A good pricing model is defined as a price that will bring no loss to neither the
provider nor the consumer. From the consumer‘s point of view a better pricing model
is one where they will pay a lower price for the resources requested, while from the
provider‘s point of view, they should not go beyond the lowest price that provides 0%
profit for them as well as increasing the utilization.
Among the research literature of price models of IT services, we have especially
studied the papers about price models of computer utility services, since utility service
is an important part of the Cloud Computing services. 50 years ago, Diamond and
Selwyn compared various price models for computer utility services, including Flat
Rate model, resource usage based model (PAYG model), connection time based
model, and transaction based model. They discussed about the different price models
from a market-oriented view, and suggested several criteria for the proper price
model, which reflected possible customer preferences. Their criteria included:
a. Cost of using the computer utility services should be predictable.
b. Users are only willing to pay for services they have actually used.
c. Users want to maximize service for given expenditure.
d. Users can pay proper share of common costs.
e. Users pay for the ―value‖ of services.
f. Users want to obtain priority service (Diamond and Selwyn, 1968).
While these criteria are useful in understanding customer behaviors in the computer
utility service market, they do not provide a systematical framework to explain and
predict which price model will be chosen under which circumstances.The argument
for homogeneous environments is that because everything comes pre-integrated they
are easier to set up, and if something goes wrong there is only one responsible party –
59
―one neck to wring‖, as the saying has it ( Wellington, 2012). Because of that we find
that users are often willing to pay a certain premium for a basic network access
service, i.e. they are willing to pay more for the same bandwidth consumption in a
Flat Rate model than in a usage-based model (PAYG model) Considering basic
network access service as a typical commodity service with nearly no heterogeneity,
we can find customers prefer a Flat Rate model for Cloud Computing services with
high homogeneity. But from the SPs‘ point of view, when services are homogeneous,
SPs are willing to provide services in a PAYG model, only if the marginal costs of
investments in a PAYG model are significantly lower than that in a Flat Rate model;
on the contrary, in a heterogeneous service market, SPs almost always prefer the
PAYG model, as long as the marginal costs of investments is not significantly higher
than that in a Flat Rate model.
The implication of this paper will be as follows: the participants generally prefer Flat
Rate model for homogeneous services and PAYG model for heterogeneous services.
Yet interesting evidence from the reality is: most utility services, which are regarded
as the most homogeneous, including electricity, water, heat, light and gas, are all
charged in a PAYG model. In fact, PAYG is regarded as ―one characteristic that
figures prominently in the utility business model and sets it apart from other models.
These partly conflicting research conclusions and realities have aroused our interest in
the actual influence of service homogeneity on the preferred price model in the Cloud
Computing markets.
3.2.2.4 Usage Frequency and Price Model
Usage frequency to be another potential influencing factor in choosing price model,
too. The reason is simple: in a world with no uncertainty, the PAYG model is clearly
a superior price model compared to Flat Rate, because no one ever needs the
guarantee and flexibility of usage provided by a Flat Rate option. From a pure cost-
efficient point of view, the Flat Rate pricing will lead to a suboptimal solution for the
Internet access service, as long as the Internet is not congestion-free, researchers have
not been unanimous about why most SPs of Internet access services choose Flat Rate,
or a price model containing Flat Rate option. Paper by Lambrecht and Skiera
summarized different explanations of this ―Flat Rate bias‖ and examined them using
empirical analysis. According to their analysis, there are three major causes of the Flat
Rate bias:
61
a. Insurance effect, which means that ―Risk-averse consumers who cannot predict
their future demand exactly can choose a flat rate to insure against the risk of
high costs in periods of greater-than-average usage.
b. Overestimation effect by the consumers.
c. Taxi meter effect‖, which means that consumers may enjoy their usage more on
a Flat Rate than on a PAYG price model (Lambrecht et al, 2006).
It is noticed that the first two causes are tightly associated with the usage uncertainty
of services; therefore, the choice of price model should be affected by the degree of
uncertainty.
The uncertainty is a complex issue: there is uncertainty about the timing, the volume,
and the length etc. of service requests. We consider the usage frequency as a good
indicator for the service uncertainty, because the need for a recurrently used service is
more observable, and therefore more predictable.
When the customers in the markets are highly concentrated and mainly low-usage
consumers, Flat Rate model is a good strategy, when the markets mature, and the
average usage level increases, the service providers should consider either increasing
their fixed fee, or shifting into PAYG model. If this assumption is true, high usage
frequency should be associated with low uncertainty, and leads to a preference for
PAYG price model.
61
Chapter 4
Methodology
Chapter Outline: 4.1 Introduction
4.2 Research Design
4.3 Research Methodology
4.3.1 Data Collection Methodology:
4.3.1.1 Secondary Data
4.3.1.2Primary Data
4.3.2 Questionnaire Content
4.3.2.1 Questionnaire Structure
4.3.3 Population and Sampling
4.4 Pilot Study
4.5 Methodology of Data Analysis
4.5.1 Data Preparation
4.5.2 Statistical Analysis Tools
4.6 Validity of the Research
4.6.1 Content Validity of the Questionnaire
4.6.2 Statistical Validity of the Questionnaire
4.6.3 Criterion Related Validity
4.6.3.1 Internal Consistency:
4.6.4 Structure Validity of the Questionnaire
4.7 Reliability of the Research
4.7.1 Cronbach’s Coefficient Alpha
4.7.2 Half Split Method
62
4.1 Introduction
This chapter describes the methodology that was used in this research. The adopted
methodology to accomplish this study uses the following techniques: the information
about the research design, research population, questionnaire design, statistical data
analysis, content validity and pilot study.
4.2 Research Design
The first phase of the research thesis proposal included identifying and defining the
problems and establishment objective of the study and development research plan.
The second phase of the research included a summary of the comprehensive literature
review. Literatures on claim management were reviewed.
The third phase of the research included a field survey which was conducted
with"Market Acceptance of Cloud Computing in Gaza IT Market (An analysis of
market structure and price models)."
The fourth phase of the research focused on the modification of the questionnaire
design, through distributing the questionnaire to pilot study, The purpose of the pilot
study was to test and prove that the questionnaire questions are clear to be answered in
a way that help to achieve the target of the study. The questionnaire was modified
based on the results of the pilot study.
The fifth phase of the research focused on distributing questionnaire. This
questionnaire was used to collect the required data in order to achieve the research
objective.
The sixth phase of the research was data analysis and discussion. Statistical Package
for the Social Sciences, (SPSS) was used to perform the required analysis.
The seventh phase of the research includes the conclusions and recommendations.
63
Figure 4.1 Illustrates the methodology flow chart
4.3 Research Methodology
4.3.1 Data Collection Methodology:
As the study follows the analytical descriptive methodology, different tools to
collect primary and secondary data were utilized as follows:
4.3.1.1 Secondary Data
To introduce the theoretical literature of the subject, the following data sources
were used:
a. Books and references in both English and Arabic about MBI and decision
making.
b. Periodicals, published papers and articles.
c. Reports and statistics
d. Web sites
Topic Selection
Literature Review
Identify the
Problem
Define the Problem
Establish Objective
Develop
Research Plan
Questionnaires
Questionnaires Design
Results and
Data Analysis
Conclusion &
Recommendation
Field Surveying
Thesis Proposal
Literature Review
Pilot
Questionnaires
Questionnaires
Validity
Questionnaires
Reliability
64
4.3.1.2 Primary Data
To collect the primary data of the research, a questionnaire was developed and
distributed to the sample of the study in order to get their opinions about
"Market Acceptance of Cloud Computing in Gaza IT Market (An analysis of
market structure and price models)."
Research methodology depends on the analysis of data on the use of descriptive
analysis, which depends on the poll and use the main program (SPSS).
4.3.2 Questionnaire content
The questionnaire was provided with a covering letter explaining the purpose of the
study, the way of responding, the aim of the research and the security of the
information in order to encourage a high response. The questionnaire included
multiple choice questions: which used widely in the questionnaire, The variety in
these questions aims first to meet the research objectives, and to collect all the
necessary data that can support the discussion, results and recommendations in the
research.
4.3.2.1 Questionnaire Structure
The behaviors of SPs in a market are often more observable than the behaviors of
service users, especially potential users. As mentioned in Chapter 3, we have found
from the composed market data that the majority of SPs in current Cloud Computing
market prefer short-term contracts to other market structures; and that the PAYG
model is their favorite price model. Nevertheless, other types of market structures, as
well as price models, have been in use among the SPs too. Thus we conclude an
optimal choice of market structure and price model is not yet found; or more possibly,
that an optimal choice exists only, when certain characteristics of service and other
factors are predetermined. These factors can have influence on SPs, customers, or
both. We also acknowledge that there is no way we can exhaust all the influencing
factors in a thesis. Therefore, as mentioned in Chapter 3.2.1 and 3.2.1.4, this thesis
focuses on two possible influencing factors: the service homogeneity and the usage
frequency.
Survey is a common tool for the purpose of testing a certain theory or causal relations
in reality. To find out the potential influences of these two factors on customer‘s
65
choice of market structures and price models, a survey is developed for focusing on
the market acceptance of Cloud Computing in Gaza IT Market. The survey is also
used for discovering more information about the customers‘ knowledge and
preferences about Cloud Computing. In accordance with achieving the aimed goal of
this study; this survey is designed in two parts:
Part one: Include the general information of study Respondents.
Part two: Include the five dimensions of the study, which are:
The first dimension (general information and knowledge of Cloud Computing):
general questions about the company (type of company, IT activities and budget)
and questions about the status quo of Cloud Computing market, including how
many companies among the respondents are already using or plan to use Cloud
Computing services, as well as their opinions on the pros and cons of Cloud
Computing services.
The second dimension (service homogeneity of IT service): questions about the
respondents‘ opinion on the service homogeneity of the IT services they use.
The third dimension (usage frequency of IT service): questions about the
respondents‘ opinion on the Usage frequency of the IT services they use.
The fourth dimension (market structure): this section contains a question about
the respondents‘ preferred market structure.
The fifth dimension ( price model): this section contains a question about the
respondents‘ preferred price model.
Questions answered in different scales illustrated in questioners.
4.3.3 Population and Sampling
The research population consists of staff that has relations in computer and IT fields
(Director, Manager, Head of Section, Head of Unit, Engineer or Administrator) at
Information technology and communication companies in the Gaza Strip that
registered with PITA (Palestinian information technology association) which
totaling 32 companies. Table 1 in Appendix D shows the companies that the
questionnaires were distributed to their employees
66
About 70 Questionnaires were distributed to the research population and 61
questionnaires are received.
4.4 Pilot Study
A pilot study for the questionnaire was conducted before collecting the results of the
sample. It provides a trial run for the questionnaire, which involves testing the
wordings of question, identifying ambiguous questions, testing the techniques that
used to collect data, and measuring the effectiveness of standard invitation to
respondents.
4.5 Methodology of Data Analysis
4.5.1 Data Preparation
All the raw data obtained from the survey are nominal or ordinal, or the so-
called ―nonmetric data‖. Typical nominal data are sex, religion, ethnicity, geographic
location etc. In our survey, the nominal data are such as the preferred market
structure, the preferred price model, and whether a service is regarded as a
homogeneous service. In statistics, data in the nominal level are usually used for
classification or categorization. Other data set from the survey are ordinal data, e.g.
the popular Likert scale (Strongly Agree – Agree – Neutral – Disagree – Strongly
Disagree), and the usage frequency of IT services (Very Frequently – Frequently –
Normal – Infrequently – Very Infrequently) employed in this survey.
These data can be used to rank or order objects. We usually transfer these data into a
reduced form, i.e. a scale of 1-5 or 1-3 before analysis, but they are still ―ordinal‖
data, because the numbers do not really represent the numerical relationship between
the options, e.g. if we assign the scale 1-5 for the Likert scale, by which Strongly
Agree = 1 and Strongly Disagree = 5, this scale does not mean that intervals between
people choosing ―Strongly Agree‖ and ―Agree‖, and the intervals between people
choosing ―Disagree‖ and ―Strongly Disagree‖, are the same.
67
4.5.2 Statistical Analysis Tools
Data analysis both qualitative and quantitative data analysis methods would be
used. The Data analysis will be made utilizing (SPSS 20). The researcher would
utilize the following statistical tools:
a. Frequencies and Percentile
b. Alpha-Cronbach Test for measuring reliability of the items of the
questionnaires
c. Person correlation coefficients for measuring validity of the items of the
questionnaires.
d. Spearman –Brown Coefficient
e. One sample t test
f. 6- Chi-square test
g. The One-Way Analysis of Variance (ANOVA)
4.6 Tests of Normality
1-Sample K-S test will be used to identify if the data follow normal distribution or
not, this test is considered necessary in case testing hypotheses as most parametric
Test stipulate data to be normality distributed and this test used when the size of the
sample are greater than or equal 50.
Results test as shown in table (17), clarifies that the calculated p-value is greater than
the significant level which is equal 0.05 ( p-value. > 0.05), this in turn denotes that
data follows normal distribution, and so parametric Tests must be used.
Table 4.1 1-Sample k-s
Section Statistic
test
P-
value
Service homogeneity of cloud computing 0.727 0.751
Market structure of cloud computing 0.849 0.316
price model 0.960 0.150
Usage frequency of cloud computing 1.045 0.224
why cloud computing seems attractive to
your company include 1.323 0.091
using Cloud Computing now or in near
future 0.475 0.978
All items 1.045 0.224
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4.7 Validity of the Research
We can define the validity of an instrument as a determination of the extent to which
the instrument actually reflects the abstract construct being examined. "Validity refers
to the degree to which an instrument measures what it is supposed to be measuring".
High validity is the absence of systematic errors in the measuring instrument. When
an instrument is valid; it truly reflects the concept it is supposed to measure.
Achieving good validity required the care in the research design and sample selection.
The amended questionnaire was by the supervisor and three expertises in the
tendering and bidding environments to evaluate the procedure of questions and the
method of analyzing the results. The expertise agreed that the questionnaire was valid
and suitable enough to measure the purpose that the questionnaire designed for.
4.7.1 Content Validity of the Questionnaire
Content validity test was conducted by consulting two groups of experts. The first was
requested to evaluate and identify whether the questions agreed with the scope of the
items and the extent to which these items reflect the concept of the research problem.
The other was requested to evaluate that the instrument used is valid statistically and
that the questionnaire was designed well enough to provide relations and tests
between variables. The two groups of experts did agree that the questionnaire was
valid and suitable enough to measure the concept of interest with some amendments.
4.7.2 Statistical Validity of the Questionnaire
To insure the validity of the questionnaire, two statistical tests should be applied. The
first test is Criterion-related validity test (Pearson test) which measures the correlation
coefficient between each item in the field and the whole field. The second test is
structure validity test (Pearson test) that used to test the validity of the questionnaire
structure by testing the validity of each field and the validity of the whole
questionnaire. It measures the correlation coefficient between one filed and all the
fields of the questionnaire that have the same level of similar scale.
69
4.7.3 Criterion Related Validity
4.7.3.1 Internal Consistency:
Internal consistency of the questionnaire is measured by a scouting sample, which
consisted of 30 questionnaires, through measuring the correlation coefficients
between each questions in one field and the whole filed.
Table 4.1 below shows the correlation coefficient and p-value for each paragraph of
the "The reason(s) why cloud computing seems attractive to your company
include(s)" and the total of the field. As show in the table the p- Values are less than
0.05 or 0.01,so the correlation coefficients of this field are significant at α = 0.01 or α
= 0.05, so it can be said that the paragraphs of this field are consistent and valid to be
measure what it was set for.
Table 4.2 The correlation coefficient between each question in the field and the whole field
(Why cloud computing seems attractive to your company include?)
No. Question Pearson coefficient p-value
1 Less capital lockup 0.608 0.000
2 Less sunk costs and separate capex & opex 0.659 0.000
3 Less administration and maintenance costs 0.706 0.000
4 High scalability of the system continuity and avilability 0.554 0.001
5 Less data loss or other security issues 0.829 0.000
6 The interoperability of cloud computing services 0.653 0.000
7 Quick integration into existing implementations 0.671 0.000
8 Less deployment time and complexity 0.730 0.000
9 Better monitoring tools and accountability of services 0.593 0.001
10 Consolidation of legacy systems 0.596 0.001
11 Environment awareness(Green IT) 0.737 0.000
Table 4.2 below shows the correlation coefficient and p-value for each paragraph of
the " Your concern(s) about using Cloud Computing now or in near future is/are:" and
the total of the field. As show in the table the p- Values are less than 0.05 or 0.01,so
71
the correlation coefficients of this field are significant at α = 0.01 or α = 0.05, so it
can be said that the paragraphs of this field are consistent and valid to be measure
what it was set for.
Table 4.3 The correlation coefficient between each question in the field and the whole field
(Your concern(s) about using Cloud Computing now or in near future is/are)
No. question Pearson
coefficient
p-
value
1 Technology immaturity 0.433 0.017
2 Technology complexity 0.372 0.043
3 Potential system failure due to hardware problems 0.482 0.007
4
Security issues (data loss, confidential information
etc.) 0.619 0.000
5 Legacy infrastructure 0.588 0.001
6 Legal compliance 0.532 0.002
7 High deployment costs 0.496 0.005
8
Lock in problem and opportunity cost by following
the wrong trend 0.446 0.014
9 Hostile software licensing regime 0.756 0.000
Table 4.3 below shows the correlation coefficient and p-value for each paragraph of
the" Which service homogeneity would you prefer for each of the following IT
service" and the total of the field. As show in the table the p- Values are less than 0.05
or 0.01,so the correlation coefficients of this field are significant at α = 0.01 or α =
0.05, so it can be said that the paragraphs of this field are consistent and valid to be
measure what it was set for.
71
Table 4.4 The correlation coefficient between each question in the field and the whole field
(service homogeneity of IT service)
No. Question Pearson
coefficient
p-
value
1 Storage, archiving and disaster recovery 0.502 0.005
2 Raw computing power (CPU, Memory etc) 0.457 0.011
3 Dedicated data center or servers (e.g. Dell, HPC etc.) 0.442 0.014
4 Basic office applications (e.g. Microsoft Office) 0.446 0.013
5 Business applications (e.g. SAP ERP system) 0.417 0.022
6 Specialized applications or solutions (e.g. simulation software
for financial industry) 0.393 0.032
7 Specialized IT services, such as security, management and
compliance 0.597 0.000
8 Cloud Operating System (e.g. Windows Azure from Microsoft) 0.532 0.002
9 Online Application Exchange Platform (e.g. Salesforce.com) 0.433 0.017
Table 4.4 below shows the correlation coefficient and p-value for each paragraph of
the" How frequently does your company use the following IT services? " and the total
of the field. As show in the table the p- Values are less than 0.05 or 0.01,so the
correlation coefficients of this field are significant at α = 0.01 or α = 0.05, so it can
be said that the paragraphs of this field are consistent and valid to be measure what it
was set for.
Table 4.5 The correlation coefficient between each question in the field and the whole field
(usage frequency of IT service)
No. Question Pearson
coefficient
p-
value
1 Storage, archiving and disaster recovery 0.748 0.000
2 Raw computing power (CPU, Memory etc) 0.737 0.000
3 Dedicated data center or servers (e.g. Dell, HPC etc.) 0.739 0.000
4 Basic office applications (e.g. Microsoft Office) 0.519 0.003
5 Business applications (e.g. SAP ERP system) 0.594 0.001
6 Specialized applications or solutions (e.g. simulation
software for financial industry) 0.387 0.035
7 Specialized IT services, such as security, management and
compliance 0.774 0.000
8 Cloud Operating System (e.g. Windows Azure from
Microsoft) 0.502 0.005
9 Online Application Exchange Platform (e.g.
Salesforce.com) 0.711 0.000
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Table 4.5 below shows the correlation coefficient and p-value for each paragraph of
the" Which transaction type would you prefer for each of the following cloud
computing service?" and the total of the field. As show in the table the p- Values are
less than 0.05 or 0.01,so the correlation coefficients of this field are significant at α =
0.01 or α = 0.05, so it can be said that the paragraphs of this field are consistent and
valid to be measure what it was set for.
Table 4.6 The correlation coefficient between each question in the field and the whole field
(Market structure)
No. Question Pearson
coefficient
p-
value
1 Storage, archiving and disaster recovery 0.619 0.000
2 Raw computing power (CPU, Memory etc) 0.588 0.001
3 Dedicated data center or servers (e.g. Dell, HPC etc.) 0.482 0.007
4 Basic office applications (e.g. Microsoft Office) 0.496 0.005
5 Business applications (e.g. SAP ERP system) 0.446 0.014
6 Specialized applications or solutions (e.g. simulation
software for financial industry) 0.756 0.000
7 Specialized IT services, such as security, management and
compliance 0.516 0.004
8 Cloud Operating System (e.g. Windows Azure from
Microsoft) 0.557 0.001
9 Online Application Exchange Platform (e.g.
Salesforce.com) 0.628 0.000
Table 4.6 below shows the correlation coefficient and p-value for each paragraph of
the" Which price model would you prefer for each of the following cloud computing
service?" and the total of the field. As show in the table the p- Values are less than
0.05 or 0.01,so the correlation coefficients of this field are significant at α = 0.01 or α
= 0.05, so it can be said that the paragraphs of this field are consistent and valid to be
measure what it was set for.
73
Table 4.7 The correlation coefficient between each question in the field and the whole field
(Price model)
No. question Pearson
coefficient
p-
value
1 Storage, archiving and disaster recovery 0.570 0.001
2 Raw computing power (CPU, Memory etc) 0.788 0.000
3 Dedicated data center or servers (e.g. Dell, HPC etc.) 0.565 0.001
4 Basic office applications (e.g. Microsoft Office) 0.554 0.002
5 Business applications (e.g. SAP ERP system) 0.673 0.000
6 Specialized applications or solutions (e.g. simulation software
for financial industry) 0.756 0.000
7 Specialized IT services, such as security, management and
compliance 0.735 0.000
8 Cloud Operating System (e.g. Windows Azure from Microsoft) 0.558 0.001
9 Online Application Exchange Platform (e.g. Salesforce.com) 0.624 0.000
4.7.4 Structure Validity of the Questionnaire
Structure validity is the second statistical test that used to test the validity of the
questionnaire structure by testing the validity of each field and the validity of the
whole questionnaire. It measures the correlation coefficient between one filed and all
the fields of the questionnaire that have the same level of liker scale.
As shown in table 4.7 the significance values are less than 0.01, so the correlation
coefficients of all the fields are significant at α = 0.01, so it can be said that the fields
are valid to be measured what it was set for to achieve the main aim of the study.
Table 4.8 Structure Validity of the Questionnaire
section
Pearson
correlation
coefficient
p-
value
Service homogeneity of cloud computing 0.792 0.000
Market structure of cloud computing 0.863 0.000
price model 0.920 0.000
Usage frequency of cloud computing 0.608 0.000
why cloud computing seems attractive to your
company include
0.716 0.000
using Cloud Computing now or in near future 0.828 0.000
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4.8 Reliability of the Research
Reliability of an instrument is the degree of consistency with which it measures the
attribute it is supposed to be measuring. The test is repeated to the same sample of
people on two occasions and then compares the scores obtained by computing a
reliability coefficient. For the most purposes reliability coefficient above 0.70 are
considered satisfactory. Period of two weeks to a month is recommended between two
tests Due to complicated conditions that the consumer is facing at the time being, it
was too difficult to ask them to responds to our questionnaire twice within short
period. The statistician's explained that, overcoming the distribution of the
questionnaire twice to measure the reliability can be achieved by using Kronpakh
Alpha coefficient and Half Split Method through the SPSS software.
4.8.1 Cronbach’s Coefficient Alpha
This method is used to measure the reliability of the questionnaire between each field
and the mean of the whole fields of the questionnaire. The normal range of
Cronbach‘s coefficient alpha value between 0.0 and + 1.0, and the higher values
reflects a higher degree of internal consistency. As shown in Table 4.8 the Cronbach‘s
coefficient alpha was calculated. The general reliability for all items equal 0.8924.
This range is considered high; the result ensures the reliability of the questionnaire.
For Reliability Table 4.9 Cronbach's Alpha
Sub-section Cronbach's
Alpha
Service homogeneity of cloud computing 0.8678
Market structure of cloud computing 0.8896
price model 0.8391
Usage frequency of cloud computing 0.9157
why cloud computing seems attractive to your
company include 0.9045
using Cloud Computing now or in near future 0.8721
All items 0.8924
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4.8.2 Half Split Method
This method depends on finding Pearson correlation coefficient between the means of
odd rank questions and even rank questions of each field of the questionnaire. Then,
correcting the Pearson correlation coefficients can be done by using Spearman Brown
correlation coefficient of correction. The corrected correlation coefficient (
consistency coefficient) is computed according to the following equation :
Consistency coefficient = 2r/(r+1), where r is the Pearson correlation coefficient. The
normal range of corrected correlation coefficient 2r/(r+1) is between 0.0 and + 1.0 As
shown in Table No.(12), and the general reliability for all items equal 0.8717, and the
significant (α ) is less than 0.05 so all the corrected correlation coefficients are
significance at α = 0.05. It can be said that according to the Half Split method, the
dispute causes group are reliable.
Table 4.10 Split-Half Coefficient method
Sub-section person-
correlation
Spearman-
Brown
Coefficient
Sig. (2-
Tailed)
Service homogeneity of cloud computing 0.7296 0.8436 0.000
Market structure of cloud computing 0.7525 0.8588 0.000
price model 0.6924 0.8182 0.000
Usage frequency of cloud computing 0.7895 0.8824 0.000
why cloud computing seems attractive to
your company include 0.8124 0.8965 0.0000
using Cloud Computing now or in near
future 0.7568 0.8616 0.0000
All items 0.7725 0.8717 0.0000
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CHAPTER Five
RESEARCH ANALYSIS AND FINDINGS
Chapter Outline:
5.1 The First Dimensions (general information)
5.1.1 Knowledge About Cloud Computing
5.1.2 IT-related Investments
5.1.3 Current Market Acceptance of Cloud Computing
5.1.4 Reason for Using Cloud Computing Services
5.1.5 Reason Against Using Cloud Computing Services
5.2 Hypothesis #1 Test (Test Statistical description of the study population)
5.2.1 Gender
5.2.2 Qualification
5.2.3 Age
5.2.4 Field of Specialization
5.2.5 Position
5.2.6 Years of Experience at this company
5.2.7 Department
5.3 Hypothesis #2 Test
5.3.1 Hypothesis a
5.3.2 Hypothesis b
5.3.3 Hypothesis c
5.3.4 Hypothesis d
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5.1 The First Dimension (general information):
5.1.1 Knowledge about Cloud Computing
One basic characteristic of the survey respondents is a basic or advanced
knowledge about Cloud Computing, which is guaranteed by the (―I am familiar with
the idea of Cloud Computing‖). If a respondent chooses the option ―Strongly
Disagree‖ for this question, the survey will be ignored (In all 61 full responses we
received, 1 of them have chosen this option).
This result shows that, despite the optimistic forecasts from many
institutions, Cloud Computing is not yet widely used in the mass market: E. M.
Rogers has proposed a 5-stages development process of technology innovation
regarding the types of main users, or so-called ―user segments‖ (Roge, 2013).
According to him, the normal development process of customers of an innovative
technology in the market is as following: ―innovators‖ ―early adopters‖ ―early
majority‖ ―late majority‖ ―laggards‖. At the first two stages of the development,
by which the main users of the technology are ―innovators‖ and ―early adopters‖
respectively, a strong ability to understand and apply complex technical knowledge is
needed, and the users are often tightly connected with the source of the innovation in
one or another way (Roge, 2013). Therefore, the majority of the survey respondents
fit perfectly into the ―innovators‖ and ―early adopters‖ categories of Rogers.
5.1.2 IT-related Investments
Figure 5.1 shows the percentage of IT-related investments to the overall revenues of
corresponding companies. It is surprise to find out that the percentage of respondents,
who confirmed that they spent more than 5% of their total revenues from the previous
year on IT-related projects, is considerably high (21.7% from the sample agrees that
company spend on IT related projects in 2014 from 5% to 20% of 2013 revenue, and
11.7% from the sample agrees that company spend on IT related projects in 2014
more than 20% of 2013 revenue).
One possible reason for the high spending on IT-related projects among the
respondents is that all of responses came from IT companies. Their high expenditure
on IT-related investments, i.e. their main business, leads to a bias in the total sample
pool.
78
Figure 5.1 Corresponding Companies‘ IT budgets in Percentage of Total Revenue from
Previous Year (2013)
5.1.3 Current Market Acceptance of Cloud Computing
Figure 5.2 shows the responses to ―the best description of Cloud Computing‘s current
role in your company is‖. The percentage of companies already using certain Cloud
Computing services is surprisingly high (46.7% of them stated they are already using
some Cloud Computing services and expect to use more; 11.7% of them stated that
they are already using some Cloud Computing services and do not expect to use
more). One possible reason for that high ratio of Cloud Computing usage is: as a new
concept, Cloud Computing has gained a range of different definitions, even from
people familiar with it. For people who consider the services like web email service as
Cloud Computing services too, it will be much easier to confirm that their companies
have already used certain Cloud Computing services. However, with the majority
among the existing users of Cloud Computing choosing ―expecting more‖, their
positive attitude towards Cloud Computing services is quite clear. Together with
another one third of the respondents saying that their companies are planning to use
Cloud Computing services, this result provides a solid evidence for the potential
growth of Cloud Computing market.
79
Figure 5.2 The Current Acceptance of Cloud Computing Services
5.1.4 Reason for Using Cloud Computing Services
Figure 5.3 shows the reasons why the users and potential users think Cloud
Computing services are attractive. We find out that the cost reason is clearly the most
influential one for buying Cloud Computing services: nearly all the respondents have
chosen ―Strongly Agree‖ or ―Agree‖ for ―less capital lockup‖, ―less sunk costs‖ and
―less administration & maintenance costs‖ as reasons for using Cloud Computing
services. We believe this is partly a result due to that Public Cloud is regarded by
many market participants as the only form of Cloud Computing: in the Software as a
Service (SaaS) and Infrastructure as a Service (IaaS) model, users do not need to
invest heavily in the applications and infrastructure in advance.
81
I
Figure 5.3 Reasons of Using Cloud Computing Services
However, in the case of a Private Cloud, service users should own the infrastructure
and applications they use in the Cloud, and there is no clear evidence that this will
leads to a reduction of capital lockup and sunk costs. Other important reasons for
using Cloud Computing services are performance oriented, such as ―system continuity
and availability‖ as well as ―high scalability of the system‖. To our best knowledge,
there is yet no empirical research on how these expectations are met by the SPs. We
have tracked the Amazon AWS to obtain a rough picture of the current system
continuity of Cloud Computing services, because Amazon AWS is widely regarded as
the most mature (Public) Cloud Computing platform.
About half of the respondents have chosen ―Strongly Agree‖ or ―Agree‖ for ―system
interoperability‖, ―less deployment time & complexity‖, ―Green IT‖, and ―less data
loss‖ as reasons for using Cloud Computing services. The first two are strongly
technical oriented subjects, which usually receive more attention in the
implementation stage. As for ―Green IT‖, the main potential contribution of Cloud
Computing is improving the utilization ratio in data centers and accelerating the data
center consolidation. However, as this survey result suggests, the idea of ―Green IT‖
does not yet enjoy a high priority by the IT-related investments at the corresponding
companies. It is hard to believe that companies treat security issues like data loss as
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trivial problem, so the result indicates that many respondents think Cloud Computing
is unable to prevent these things from happening. This is also confirmed by the
question about customers‘ concerns for Cloud Computing, by which the ―security
issue‖ received most attention from the respondents.
The least chosen reasons for using Cloud Computing services are ―monitoring tools
and accountability‖, ―quick integration‖ and ―consolidation of legacy systems‖.
Despite the inherent monitoring tools of those Cloud Computing platforms, the only
third-party monitoring tool we know is provided by Right Scale, for Amazon AWS.
As for the latter two reasons, which are in fact associated with each other, more
researches are needed to confirm these advantages of Cloud Computing compared to
traditional IT services.
5.1.5 Reason Against Using Cloud Computing Services
Figure 5.4 shows the concerns of users and potential users for Cloud Computing
services. We see the biggest concern among the responses is the ―security issue‖.
Since the users of Cloud Computing services do not always own the infrastructure and
applications (as in the case of Public Cloud and Hybrid Model), they have easily the
concern of where their data are stored, and whether they are secure. The security
issues are addressed in some SPs‘ service agreement or description, such as at
Amazon AWS. The Amazon AWS uses a range of security measures to mitigate the
potential risk, including SOX79 certification, physical security in data center, and
backup services.
Figure 5.4 Concerns of Using Cloud Computing Services
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However, this survey result shows that users and potential users are not yet convinced
by the effort made. This finding is also consistent with that from J. Staten, who said
that many enterprises are not using Cloud Computing services because they are not
secure enough. (Stat, 2012) The nest things bother users of Cloud Computing are the
―technology immaturity‖ and ―technology complexity‖: more than 70 % of the
respondents either agree or strongly agree that these are concerns against using Cloud
Computing services.
Although many of the technologies supporting Cloud Computing are already mature,
e.g. the virtualization technology, but the technology immaturity of Cloud Computing
as a whole is partly confirmed by the relatively frequent system outages we
mentioned in Chapter 5.2.1.2, as well as by the characteristics of current users (i.e.
mainly ―innovators‖ and ―early adopters‖). More controversial is the problem about
technology complexity: while the unanimous definitions of Cloud Computing, the
lack of interoperability between current Cloud Computing platforms, and generally
the immature stage of technology development do increase the complexity of Cloud
Computing for the users and potential users, Cloud Computing actually promises a lot
of simplicity: e.g. the users should not care about where exactly the data are hold,
have an ubiquitous access to the data and services they need, and enjoy a great usage
flexibility because the high scalability of their systems. The survey result shows that
the respondents are not yet convinced by the benefits mentioned above. More research
efforts are needed, to find out whether they can ―simplify‖ Cloud Computing for the
customers in the long run. Nearly 64% of the respondents believe there can be certain
―lock-in‖ problem by the Cloud Computing services. The lock-in problem occurs
when the customers of a certain SP are unable to change the SP, or can only do that
with prohibitively high costs of money or time, so that they are forced to stay in
contracting relationship with this SP. The lock-in problem is one form of ex post
transaction cost in the Transaction Cost Theory. (Williamson, 1979) For Cloud
Computing services, this problem is represented by the lack of standards and
interoperability between systems. Generally, the standardization of Cloud Computing
systems in both interface level and technical level has not yet received much attention.
To our best knowledge, there are quite few customers of Cloud Computing
already replaced their IT systems with the new Cloud Computing services.
83
As mentioned in Chapter 3.1.3, the most current users are using Cloud Computing
services for their non-core IT activities. In this case, legacy infrastructure can hardly
be a problem, but it does not mean that in the future, when Cloud Computing is
becoming a massively adopted IT practices, consolidating the legacy infrastructure
will still be a trivial task.
The least concerned problem by the respondents is the potential ―high deployment
costs‖. The respondents tend to believe that Cloud Computing is not associated with
high deployment costs at all. Combined with the results from Chapter 5.2.1.5, the
survey shows that at this time, the biggest attraction of Cloud Computing seems to be
the cost advantages.
5.2 Hypothesis #1 Test (Test Statistical description of the study population)
The main hypothesis stated that there is a statistically significant
differences attributed to the personal information of the respondents at the level of α =
0.05 about market acceptance of cloud computing in Gaza IT market.
And these hypothesis divided into sub-hypotheses as follows:
5.2.1 Gender
"There is a statistically significant differences at the level of α ≤ 0.05 about
market acceptance of cloud computingin Gaza it market refer to gender"
To test the hypothesis the Independent Samples Test is used and the result illustrated
in table no.(36) which show that the p-value equal 0.495 which is greater than 0.05
that‘s mean there is no statistically significant differences at the level of α = 0.05
about market acceptance of cloud computing in Gaza it market refer to gender .
Table 5.1 Independent Samples Test for differences about market acceptance of cloud computing in
Gaza it market refer to gender
Field Gender N Mean
Std.
Deviation
T P-
value
Service homogeneity of cloud
computing
Male 49 2.333 0.329
-1.496 0.140
female 11 2.495 0.295
Market structure of cloud
computing
Male 49 2.687 0.571
-0.262 0.794
female 11 2.737 0.593
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price model Male
49 2.766 0.493
-1.715 0.092
female 11 3.040 0.401
Usage frequency of cloud
computing
Male 49 3.859 0.943
-0.949 0.347
female 11 4.162 1.011
why cloud computing seems
attractive to your company
include
Male 49 3.770 0.966
1.227 0.225
female 11 3.405 0.364
using Cloud Computing now
or in near future
Male 49 3.494 0.486
-0.251 0.803
female 11 3.535 0.506
All items Male
49 3.152 0.338
-0.687 0.495
female 11 3.229 0.333
5.2.2 Qualification
"There is a statistically significant differences at the level of α ≤ 0.05 about
market acceptance of cloud computing in Gaza it market refer to Qualification"
To test the hypothesis the one way ANOVA is used and the result illustrated in
table no.(37) which show that the p-value equal 0.139 which is greater than 0.05 ,
that‘s means There is no statistically significant differences at the level of α = 0.05
about market acceptance of cloud computing in Gaza it market refer to Qualification.
Table 5.2 One way ANOVA test for differences about market acceptance of cloud computing
in Gaza it market refer to Qualification
Field Source
Sum of
Squares df
Mean
Square
F
value
Sig.(P-
Value)
Service homogeneity of cloud
computing
Between Groups 0.904 2 0.452 4.760
0.012
Within Groups 5.414 57 0.095
Total 6.318 59
Market structure of cloud
computing
Between Groups 0.027 2 0.014 0.040
0.961
Within Groups 19.155 57 0.336
Total 19.182 59
price model
Between Groups 0.690 2 0.345 1.481
0.236
Within Groups 13.281 57 0.233
Total 13.971 59
Usage frequency of cloud
computing
Between Groups 1.557 2 0.778 0.851
0.433
Within Groups 52.156 57 0.915
Total 53.713 59
why cloud computing seems
attractive to your company
include
Between Groups 0.349 2 0.174 0.212
0.810
Within Groups 46.922 57 0.823
Total 47.271 59
85
using Cloud Computing now or
in near future
Between Groups 1.667 2 0.833 3.879
0.026
Within Groups 12.246 57 0.215
Total 13.913 59
All items
Between Groups 0.446 2 0.223 2.046
0.139
Within Groups 6.213 57 0.109
Total 6.659 59
5.2.3 Age
"There is a statistically significant differences at the level of α ≤ 0.05 about
market acceptance of cloud computingin Gaza it market refer to Age"
To test the hypothesis the one way ANOVA is used and the result illustrated in
table no.(38) which show that the p-value equal 0.547 which is greater than 0.05 ,
that‘s means There is no statistically significant differences at the level of α = 0.05
about market acceptance of cloud computing in Gaza it market refer to Age.
Table 5.3 One way ANOVA test for differences about market acceptance of cloud computing in Gaza it market refer to Age
Field Source
Sum of
Squares df
Mean
Square
F
value
Sig.(P-
Value)
Service homogeneity of cloud
computing
Between Groups 0.558 2 0.279 2.759
0.072
Within Groups 5.760 57 0.101
Total 6.318 59
Market structure of cloud
computing
Between Groups 0.316 2 0.158 0.478
0.623
Within Groups 18.866 57 0.331
Total 19.182 59
price model
Between Groups 0.817 2 0.408 1.770
0.180
Within Groups 13.154 57 0.231
Total 13.971 59
Usage frequency of cloud
computing
Between Groups 0.911 2 0.455 0.492
0.614
Within Groups 52.802 57 0.926
Total 53.713 59
why cloud computing seems
attractive to your company
include
Between Groups 0.204 2 0.102 0.124
0.884
Within Groups 47.066 57 0.826
Total 47.271 59
using Cloud Computing now or
in near future
Between Groups 0.273 2 0.136 0.569
0.569
Within Groups 13.641 57 0.239
Total 13.913 59
All items
Between Groups 0.140 2 0.070 0.611
0.547
Within Groups 6.520 57 0.114
Total 6.659 59
86
5.2.4 Field of Specialization
"There is a statistically significant differences at the level of α ≤ 0.05
about market acceptance of cloud computingin Gaza it market refer to Field of
Specialization"
To test the hypothesis the one way ANOVA is used and the result illustrated in
table no.(39) which show that the p-value equal 0.938 which is greater than 0.05 ,
that‘s means There is no statistically significant differences at the level of α = 0.05
about market acceptance of cloud computing in Gaza it market refer to Field of
Specialization.
Table 5.4 One way ANOVA test for differences about market acceptance of cloud computing
in Gaza it market refer to Field of Specialization
Field Source
Sum of
Squares df
Mean
Square
F
value
Sig.(P-
Value)
Service homogeneity of cloud
computing
Between Groups 0.118 2 0.059 0.541
0.585
Within Groups 6.200 57 0.109
Total 6.318 59
Market structure of cloud
computing
Between Groups 0.406 2 0.203 0.617
0.543
Within Groups 18.776 57 0.329
Total 19.182 59
price model
Between Groups 0.530 2 0.265 1.125
0.332
Within Groups 13.441 57 0.236
Total 13.971 59
Usage frequency of cloud
computing
Between Groups 0.770 2 0.385 0.414
0.663
Within Groups 52.943 57 0.929
Total 53.713 59
why cloud computing seems
attractive to your company
include
Between Groups 1.173 2 0.586 0.725
0.489
Within Groups 46.098 57 0.809
Total 47.271 59
using Cloud Computing now or
in near future
Between Groups 0.571 2 0.286 1.221
0.303
Within Groups 13.342 57 0.234
Total 13.913 59
All items
Between Groups 0.015 2 0.008 0.065
0.937
Within Groups 6.644 57 0.117
Total 6.659 59
87
5.2.5 Position
"There is a statistically significant differences at the level of α ≤ 0.05
about market acceptance of cloud computingin Gaza it market refer to Position"
To test the hypothesis the one way ANOVA is used and the result illustrated in
table no.(40) which show that the p-value equal 0.338 which is greater than 0.05 ,
that‘s means There is no statistically significant differences at the level of α = 0.05
about market acceptance of cloud computing in Gaza it market refer to Position.
Table 5.5 One way ANOVA test for differences about market acceptance of cloud computing
in Gaza it market refer to Position Field
Source Sum of
Squares df
Mean
Square
F
value
Sig.(P-
Value)
Service homogeneity of cloud
computing
Between Groups 1.382 5 0.276 3.023
0.018
Within Groups 4.936 54 0.091
Total 6.318 59
Market structure of cloud
computing
Between Groups 1.911 5 0.382 1.195
0.324
Within Groups 17.271 54 0.320
Total 19.182 59
price model
Between Groups 2.218 5 0.444 2.038
0.088
Within Groups 11.753 54 0.218
Total 13.971 59
Usage frequency of cloud
computing
Between Groups 2.495 5 0.499 0.526
0.755
Within Groups 51.218 54 0.948
Total 53.713 59
why cloud computing seems
attractive to your company
include
Between Groups 6.940 5 1.388 1.859
0.117
Within Groups 40.330 54 0.747
Total 47.271 59
using Cloud Computing now or
in near future
Between Groups 2.219 5 0.444 2.049
0.086
Within Groups 11.694 54 0.217
Total 13.913 59
All items
Between Groups 0.649 5 0.130 1.166
0.338
Within Groups 6.010 54 0.111
Total 6.659 59
88
5.2.6 Years of Experience
"There is a statistically significant differences at the level of α ≤ 0.05 about
market acceptance of cloud computingin Gaza it market refer to Years of
Experience"
To test the hypothesis the one way ANOVA is used and the result illustrated in
table no.(41) which show that the p-value equal 0.901 which is greater than 0.05 ,
that‘s means There is no statistically significant differences at the level of α = 0.05
about market acceptance of cloud computing in Gaza it market refer to Years of
Experience.
Table 5.6 One way ANOVA test for differences about market acceptance of cloud computing
in Gaza it market refer to Years of Experience Field
Source Sum of
Squares df
Mean
Square
F
value
Sig.(P-
Value)
Service homogeneity of cloud
computing
Between Groups 0.634 3 0.211 2.081
0.113
Within Groups 5.684 56 0.101
Total 6.318 59
Market structure of cloud
computing
Between Groups 0.453 3 0.151 0.451
0.717
Within Groups 18.729 56 0.334
Total 19.182 59
price model
Between Groups 0.408 3 0.136 0.561
0.643
Within Groups 13.563 56 0.242
Total 13.971 59
Usage frequency of cloud
computing
Between Groups 0.790 3 0.263 0.279
0.841
Within Groups 52.923 56 0.945
Total 53.713 59
why cloud computing seems
attractive to your company
include
Between Groups 0.999 3 0.333 0.403
0.751
Within Groups 46.271 56 0.826
Total 47.271 59
using Cloud Computing now or
in near future
Between Groups 0.475 3 0.158 0.659
0.581
Within Groups 13.439 56 0.240
Total 13.913 59
All items
Between Groups 0.068 3 0.023 0.193
0.901
Within Groups 6.591 56 0.118
Total 6.659 59
89
5.2.7 Department
"There is a statistically significant differences at the level of α ≤ 0.05 about
market acceptance of cloud computingin Gaza it market refer to Department"
To test the hypothesis the one way ANOVA is used and the result illustrated in table
no.(42) which show that the p-value equal 0.679 which is greater than 0.05 , that‘s
means There is no statistically significant differences at the level of α = 0.05 about
market acceptance of cloud computing in Gaza it market refer to Department.
Table 5.7 One way ANOVA test for differences about market acceptance of cloud computing in Gaza it market refer to Department
Field Source
Sum of
Squares df
Mean
Square
F
value
Sig.(P-
Value)
Service homogeneity of cloud
computing
Between Groups 0.344 4 0.086 0.791
0.536
Within Groups 5.974 55 0.109
Total 6.318 59
Market structure of cloud
computing
Between Groups 0.730 4 0.182 0.544
0.704
Within Groups 18.452 55 0.335
Total 19.182 59
price model
Between Groups 0.771 4 0.193 0.803
0.529
Within Groups 13.200 55 0.240
Total 13.971 59
Usage frequency of cloud
computing
Between Groups 4.313 4 1.078 1.201
0.321
Within Groups 49.399 55 0.898
Total 53.713 59
why cloud computing seems
attractive to your company
include
Between Groups 2.611 4 0.653 0.804
0.528
Within Groups 44.660 55 0.812
Total 47.271 59
using Cloud Computing now or
in near future
Between Groups 0.476 4 0.119 0.487
0.745
Within Groups 13.438 55 0.244
Total 13.913 59
All items
Between Groups 0.269 4 0.067 0.579
0.679
Within Groups 6.390 55 0.116
Total 6.659 59
91
5.3 Hypothesis #2 Test
In the following tables a one sample t test is used to test if the opinion of the
respondent in the content of the sentences are positive ( weight mean greater than
"60%" and the p-value less than 0.05) or the opinion of the respondent in the content
of the sentences are neutral ( p- value is greater than 0.05) or the opinion of the
respondent in the content of the sentences are negative (weight mean less than "60%"
and the p-value less than 0.05)
5.3.1 Hypothesis a
There is a statistical significant relation between the service homogeneity of
cloud computing services and the market structure of cloud computing
services (at level of significance α≤ 0.05).
Chi-Square Tests are used to examine the correlation between service homogeneity of
IT service and market structure of cloud computing at significance level α = 0.05, and
cross-table 5.8 shows the frequency ant percentile, also table 5.9 shows that the chi-
square test equal 30.631, p-value =0.000 < 0.05, so there is a significant correlation
between Service homogeneity of IT service and Market structure of cloud computing
at significance level α = 0.05.
Table 5.8 Service homogeneity * Market structure Crosstabulation
Market structure of cloud computing
Total
Short term
transaction
Long term
transaction
In-house
transaction
No
answer
Ser
vic
e ho
mog
enei
ty o
f IT
serv
ice
homogeneos
Count 69 75 90 40 274
% of
Total 12.8% 13.9% 16.7% 7.4% 50.7%
heterogeneos
Count 50 28 80 30 188
% of
Total 9.3% 5.2% 14.8% 5.6% 34.8%
No answer
Count 13 25 40 0 78
% of
Total 2.4% 4.6% 7.4% .0% 14.4%
Total Count
132 128 210 70 540
% of
Total 24.4% 23.7% 38.9% 13.0% 100.0%
91
Table 5.9 Chi-Square Tests
Chi-Square Tests Value df
Asymp. Sig. (2-
sided)
Pearson Chi-Square 30.631a 6 .000
Likelihood Ratio 41.348 6 .000
Linear-by-Linear Association .094 1 .759
N of Valid Cases 540
Figure 5.6 Service homogeneity * Market structure
5.3.2 Hypothesis b
There is a statistical significant relation between the usage frequency of IT
service and the market structure of cloud computing services (at level of
significance α≤0.05).
Chi-Square Tests are used to examine the correlation between usage frequency of IT
service and market structure of cloud computing at significance level α = 0.05, and
cross-table 5.10 show the frequency ant percentile, also table 5.11 show that the chi-
square test equal 54.347, p-value =0.000 < 0.05, so there is a significant correlation
between usage frequency of IT service and Market structure of cloud computing at
significance level α = 0.05.
92
Table 5.10 Usage frequency * Market structure Crosstabulation
Usage frequency of IT service
Total
Very
frequently
(many times
in a day)
Frequently
(daily)
Normal(daily-
weekly)
Infrequent
(monthly)
Very
Infrequent(rare)
No
answer
Market
structure of
cloud
computing
Short term
transaction
Count 21 44 25 9 13 20 132
% of
Total 3.9% 8.1% 4.6% 1.7% 2.4% 3.7% 24.4%
Long term
transaction
Count 24 30 13 15 12 10 104
% of
Total 4.4% 5.6% 2.4% 2.8% 2.2% 1.9% 19.3%
In-house
transaction
Count 47 60 35 25 16 25 208
% of
Total 8.7% 11.1% 6.5% 4.6% 3.0% 4.6% 38.5%
No answer Count 10 11 41 9 15 10 96
% of
Total 1.9% 2.0% 7.6% 1.7% 2.8% 1.9% 17.8%
Total Count 102 145 114 58 56 65 540
% of
Total 18.9% 26.9% 21.1% 10.7% 10.4% 12.0% 100.0%
Table 5.11 Chi-Square Tests
Chi-Square Tests Value df
Asymp. Sig. (2-
sided)
Pearson Chi-Square 54.347a 15 .000
Likelihood Ratio 52.919 15 .000
Linear-by-Linear Association .882 1 .348
N of Valid Cases 540
93
Figure 5.7 Usage frequency * Market structure
5.3.3 Hypothesis c
There is a statistical significant relation between the service homogeneity of
IT service and the price model of cloud computing services (at level of
significance α≤0.05).
We use Chi-Square Tests to examine the correlation between Service homogeneity of
IT service and price model at significance level α = 0.05, and cross-table 5.12 show
the frequency ant percentile, also table 5.13 show that the chi-square test equal
10.718, p-value =0.098 > 0.05, so there is no significant correlation between Service
homogeneity of IT service and price model at significance level α = 0.05.
94
Table 5.12 Service homogeneity * price model Crosstabulation
price model
Total
Flat
Pricing
Pay as you go
Pricing
Mixture of Flat & Pay as
you go
No
answer
Ser
vic
e ho
mog
enei
ty o
f IT
ser
vic
e
Homogene
ity
Count 82 105 60 27 274
% of
Total 15.2% 19.4% 11.1% 5.0% 50.7%
heterogene
ity
Count 55 72 42 19 188
% of
Total 10.2% 13.3% 7.8% 3.5% 34.8%
No answer Count 22 40 16 0 78
% of
Total 4.1% 7.4% 3.0% 0.0% 14.4%
Total Count 159 217 118 46 540
% of
Total 29.4% 40.2% 21.9% 8.5% 100.0%
Table 5.13 Chi-Square Tests
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 10.718a 6 .098
Likelihood Ratio 17.117 6 .009
Linear-by-Linear Association 1.598 1 .206
N of Valid Cases 540
Figure 5.8 Service homogeneity * price model
95
5.3.4 Hypothesis d
There is a statistical significant relation between the usage frequency of IT
services and the price model of cloud computing services (at level of
significance α≤0.05).
We use Chi-Square Tests to examine the correlation between usage frequency of IT
services and price model at significance level α = 0.05, and cross-table 5.14 show the
frequency ant percentile, also table 5.15 show that the chi-square test equal 48.559, p-
value =0.000 < 0.05, so there is a significant correlation between price model and
Usage frequency of IT services at significance level α = 0.05
Table 5.14 usage frequency* price model Crosstabulation
Usage frequency of cloud computing
Total
Very
frequently
(many
times in a
day)
Frequentl
y (daily)
Normal(daily-
weekly)
Infrequent
(monthly)
Very
Infrequent(rare)
No
answer
pri
ce m
od
el
Flat
Pricing
Count 30 42 34 10 20 45 181
% of
Total 5.6% 7.8% 6.3% 1.9% 3.7% 8.3% 33.5%
Pay as
you go
Pricing
Count 60 45 29 20 10 18 182
% of
Total 11.1% 8.3% 5.4% 3.7% 1.9% 3.3% 33.7%
Mixture
of Flat &
Pay as
you go
Count 17 33 32 15 10 14 121
% of
Total 3.1% 6.1% 5.9% 2.8% 1.9% 2.6% 22.4%
No
answer
Count 9 19 14 3 4 7 56
% of
Total 1.7% 3.5% 2.6% .6% .7% 1.3% 10.4%
Total Count 116 139 109 48 44 84 540
% of
Total 21.5% 25.7% 20.2% 8.9% 8.1% 15.6% 100.0%
96
Table 5.15 Chi-Square Tests
Chi-Square Tests Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 48.559a 15 .000
Likelihood Ratio 46.825 15 .000
Linear-by-Linear Association 4.683 1 .030
N of Valid Cases 540
Figure 5.9 Usage frequency*price model
97
Chapter Six
Results & Further Research Directions
Chapter Outline:
6.1 Introduction:
6.2 Research Results
6.2.1 QuestionnaireParagraphs
6.2.2 Relation of Research Variables
6.3 Evaluation of research methodology
6.4 Concluding Remarks and Further Research Directions
98
6.1 Introduction:
The main propose of this thesis is to study the current and future market
acceptance of Cloud Computing regarding the choice of market structure and price
model, in light of service homogeneity and usage frequency of the IT services in Gaza
IT market. As well as to measure the effects of the demographic factors such as
gender, age, qualifications, type of position, position, years of experience.
The findings of applied and field study were obtained through collected
questionnaires field study, unloading operations, conduct appropriate statistical
hypothesis testing, and extraction and presentation of results. Then make the
necessary recommendations and suggestions that would help Gaza IT market to take
advantage of Cloud Computing Technology to improve and develop their
organizations. Finally, setting of proposals for future studies that could be conducted.
6.2 Research Results
Through the results of the statistical analysis of the respondent's views, the most
important findings of this study could be summarizing as following:
6.2.1 Questionnaire paragraphs
a. Familiarity of Cloud Computing:
35.0% from the sample are familiar strongly with the idea of Cloud
Computing, and 63.3 % from the sample are familiar with the idea of Cloud
Computing. Only 1.6% from the sample aren't familiar with the idea of Cloud
Computing.
b. IT-related investments
21.7% from the sample agrees that company spend on IT related projects in
2014 from 5% to 20% of 2013 revenue, and 11.7% from the sample agrees that
company spend on IT related projects in 2014 more than 20% of 2013 revenue. One
possible reason for the high spending on IT-related projects among the respondents is
that the majority of the responses came from IT companies.
99
c. Current Market Acceptance of Cloud Computing
46.7% of responses said they are already using some Cloud Computing
services and expect to use more; 11.7% of responses said that they are already using
some Cloud Computing services and do not expect to use more). One possible reason
for that high ratio of Cloud Computing usage is: as a new concept, Cloud Computing
has gained a range of different definitions, even from people familiar with it.
d. Reason for using Cloud Computing services
We find out that the cost reason is clearly the most influential one for
buying Cloud Computing services: nearly all the respondents have chosen ―Strongly
Agree‖ or ―Agree‖ for ―less capital lockup‖, ―less sunk costs‖ and ―less
administration & maintenance costs‖ as reasons for using Cloud Computing services.
The least chosen reasons for using Cloud Computing services are ―monitoring tools
and accountability‖, ―quick integration‖ and ―consolidation of legacy systems‖.
e. Reason against using Cloud Computing services
The biggest concern among the responses is the ―security issue‖. Nearly
64% of the respondents believe there can be certain ―lock-in‖ problem by the Cloud
Computing services The least concerned problem by the respondents is the potential
―high deployment costs‖. The respondents tend to believe that Cloud Computing is
not associated with high deployment costs at all
f. service homogeneity of IT service
Table 1 in Appendix C shows that 50.74% of the respondents prefer
homogeneous IT services and only 34.81% of the respondents of the respondents
prefer heterogeneous IT services. Heterogeneity makes it hard for a firm to
standardize the quality of its services. Opposite of homogeneity.
g. Usage frequency of IT service
A summary of the usage frequency of various IT services is shown in
Table 2 in Appendix C. Not surprisingly, the most frequently-used IT services are
basic office applications (e.g. Microsoft Office software), raw computing resources
(servers, storage discs and bandwidth etc.), and business applications (ERP software,
CRM software etc.). Although we know that these data cannot fully represent the
usage frequency of equivalent Cloud Computing services, we do notice that these
111
services are among the first offered Cloud Computing services in the market. As
shown in the Table 3.1 in Appendix A, companies like Google and Zoho are the
pioneers providing online documents editing services, as an equivalent for the
traditional Microsoft Office® software. Although these services are not yet widely
accepted by large enterprises, it does offer the individuals an alternative for buying
the software from Microsoft. As for business applications, we have already described
the success of Salesforce.com on the On-Demand CRM application market in Chapter
3.1. And the situation by raw computing resources is even more obvious: the most
Cloud Computing service providers on the current market are providing some sort of
storage, backup, or synchronization services. So we believe that the Cloud Computing
services on the current market match quite well the need of customers and potential
customers for general IT services.
Compared to the services mentioned above, much fewer respondents said their
companies use specialized applications and special IT services frequently. This is
understandable because these services are ―special‖, which means they are used only
for certain proposes, products or customers. We have also observed that even fewer
companies are starting to use Cloud Operating System. The Cloud Operating Systems
are not necessarily an equivalent for Windows or Linux system. The word
―Operating‖ here has a wider range of meaning. These systems work in a distributed
system, or between many distributed systems, and are used as a platform for
managing applications as well as resources in a network.
h. Market structure
Table 3 in Appendix C shows that the percent of short term transaction is
24.4% , and the percent of Long term transaction is 23.7% , and the percent of In-
house transaction is 38.9%.
The high percent of In-house transaction means the buyers prefer not only to receive
the services, but also to own the whole products and infrastructure, therefore gain the
whole control of the service activity.
i. Price model
Table 4 in Appendix C shows that the percent of Flat Pricing is 29.4% , and
percent of Pay as you go model is 40.2% , and percent of Mixture of Flat & Pay as
111
you go is 21.9% , and percent of No answer = 8.5%.The high percent of Pay as you
go model means that the users prefer to charge according to their actual usage of
resources.
6.2.2 Hypothesis Testing Results
a. There is a significant correlation between service homogeneity of IT service
and Market structure of cloud computing at significance level α = 0.05.
b. There is a significant correlation between usage frequency of IT service and
Market structure of cloud computing at significance level α = 0.05.
c. There is no significant correlation between service homogeneity of IT service
and price model at significance level α = 0.05.
d. There is a significant correlation between price model and Usage frequency of
IT services at significance level α = 0.05
6.2.3 Answers of research questions
a. "What's the potential influence of the homogeneity of cloud computing services
on customer’s choice of market structures of cloud computing services?"
According to table 5.8, customers prefer In-house transaction for homogeneous IT
services and try to avoid long term transaction for heterogeneous IT services.
b. "What's the potential influence of usage frequency of cloud computing services
on customer’s choice of market structures of cloud computing services?"
According to table 5.10, customers prefer In-house for all kind of IT services, when
the usage frequency is high.
c. "What's the potential influence of the homogeneity of cloud computing services
on customer’s choice of price model of cloud computing services?"
According to table 5.12, the homogeneity of cloud computing services doesn't effect
on customer's choice of price model.
d. "What's the potential influence of usage frequency of cloud computing services
on customer’s choice of price model of cloud computing services?"
112
According to table 5.14, customers prefer PAYG model when the usage frequency of
cloud computing services is high.
6.3 Evaluation of Research Methodology
AS it is acknowledged that the service homogeneity and the usage frequency are
not the only influencing factors for market structure and price model. For example,
security issues may cause general concerns about the implementation of Cloud
Computing outside the company, therefore users and potential users may prefer to use
in-house Cloud Computing solutions, even when the services are highly
homogeneous, and the transaction cost of obtaining the service from open market may
be lower. While considering all these potential influencing factors is far beyond the
scope of a master thesis, it seems there are certainly other factors worth further
research efforts.
6.4 Concluding Remarks and Further Research Directions
This is the first empirical study in the market acceptance of Cloud
Computing services in Gaza regarding the market structures and price models. Based
on the customer survey, there are the following findings:
a. Generally, In Gaza the Cloud Computing market is still at its early stage of
development. The main users in the market are so-called ―innovators‖ and ―early
adopters‖, and users still have many concerns facing the uncertainty of the
technology evolvement as well as the business model development. However, the
general attitude toward Cloud Computing services among the users and potential
users is very positive.
b. Service homogeneity serves as a good indicator for the preferred market structure
of certain Cloud Computing service. Generally, the users and potential users tend
to choose open market transaction, i.e. Public Cloud for homogeneous services,
and in-house transaction, i.e. Private Cloud for heterogeneous services.
c. The usage frequency does have certain influence on the preferred price model.
Users tend to choose PAYG model for high-frequency services, and Flat Rate
model for low-frequency services. Since the correlation between the usage
frequency and price model is not extremely high, we recommend further
113
investigation of the potential influencing factors on price models of Cloud
Computing services.
d. Compared to the preferences from users and potential users of Cloud Computing
services provided in the market match well their general need for IT services, but
not the current need for Cloud Computing services.
The services mostly promoted by the SPs, are the services with high usage
frequency too, such as raw computing resources, basic office applications and
business applications, but currently, most companies are not using Cloud
Computing services for their core IT activities. While this mismatch can be solved
in the market development of Cloud Computing in the future, it does have
negative influence on the SPs‘ profitability by now.
This thesis can deliver hints for the development of Cloud Computing market as well
as for further theoretical analyses in the future.
114
References
115
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111
Appendices
111
Appendix (A) List of SPs
Companie
s
Acti
ve/
Beta
A/P/
R
/T
PAYG
/Mixtur
e
/Flat
Rate
Service / Products Notes
10Gen B P, A Hosting service Open source
37signals A A CRM solutions
3Tera A R, T Grid Hosting, AppLogic System
Adobe
Acrobat B A Collaboration solutions
Akamai A A, T Application Performance
Solutions
Amazon
AWS R PAYG
Cloud Computing ecosystem, (EC2,
S3, SimpleDB, SQS, and FPS)
Cooperation with
Salesforce
Aptana B R, P PAYG Computing service, "Aptana Studio"
(platform)
Areti
(Alentus) A R Mixture
Grid hosting (Ares), managed hosting,
co-location
Using 3Tera's
AppLogic
AT&T A R Managed hosting
Cassatt A A, Hosting, Utility Computing
(―Cassatt Active Response‖)
112
Cisco
Systems A
A,
T,
P
WebEx Connect platform, Data Center
solutions
Citrix
(inc.
XenSourc
e)
A A, T Dynamic Application Delivery System,
Citrix Cloud Center
Cloudwor
k
s
A R, A PAYG Storage service and backups Supported by Citrix
cohesiveF
T A P, T
Development platform, VM
Management
software
Dell A R, T Flat Rate Dell Cloud Computing solutions
Elastra A
R,
P,
T
PAYG "Elastic computing", system
monitoring tools
Supported by Amazon
S3
EMC (inc.
VMware
&
Mozy)
A
R,
T,
A
storage & backup service, data center
solutions
Enki A R PAYG ―Computing Utility‖ (Private Data
Centers), co-location
Using 3Tera's
AppLogic
Enomaly B T "Enomalism Cloud Computing" Open source
Eucalyptu
s A T Eucalyptus Public Cloud Open source
FlexiScale
(Xcalibre) A R PAYG Server hosting
113
Fortress
ITX A R
Managed hosting,
co-location
Using 3Tera's
AppLogic
Gh.o.st B A Virtual desktop
Supported by
Amazon S3
GoGrid/
ServePath B R PAYG
Grid hosting, ―Cloud
Connect‖, storage
Google A R, P PAYG App Engines (platform),
storage
Python
Environment
IBM A A, T Flat Rate "Blue Cloud",
"Bluehouse"
Joyent A R, A Mixture Computing and storage
solution, Web application platform
Microsoft
(Azure
platform
etc.)
A R,
A, P
Azure platform, Collaboration
solutions,
ECM, Exchange Hosted Services,
CRM
Mosso A P Mixture Cloud storage, web hosting
NetSuite A A CRM, ERP and eCommerce
Project
Caroline
(SUN)
B P ―Platform as a Service‖ (PaaS) Open source
QuickBas
e A P, A Mixture
Online project management, online
CRM
etc.
Right
Scale A A, T Flat Rate Cloud computing Management
Based on
Amazon AWS
114
Salesforce
A P, A Mixture "AppExchange" (platform)
SUN
Network.c
om
A R, A Utility Computing (Network.com)
Terremark A R PAYG Managed hosting, co-location
Member of
"Green Grid"
Workday A A HR management, financial
management etc.
Zoho A P, A Online document software, CRM
software, Zoho Marketplace
115
Appendix (B)
Final Questionnaire in English
Islamic University of Gaza
Dean of Postgraduate Studies
Faculty of Commerce
Department of Business Administration
Questionnaire
Dear All…...
The researcher puts in your hands this questionnaire prepared for collecting data about
a study entitled:
"Market Acceptance of Cloud Computing in Gaza IT Market
(An analysis of market structure and price models)"
Which this study be submitted in a partial fulfillment of the requirement for MBA degree,
I hope you to cooperate and provide information to assist in the completion of this study.
The questionnaire aim to find out the potential influences of service homogeneity of
cloud computing and the usage frequency of cloud computing on customer‘s choice of
market structures and price models to focus on Market Acceptance of Cloud Computing
in Gaza IT Market.
As you have the experience and professional in your work field, and also your currently
position which related to the subject of the research, the researcher request you to see all
questionnaire items in carefully ,and answer all of them in objectively and high
professional. Your feedback and comments would be a matter of interest and they will
have great impact regarding the enrichment of this study. Please note that its use will be
limited to scientific research purposes. Moreover, the questionnaire will be treated
confidentially.
Definition of Cloud Computing:
Cloud computing is computing environment or service model that enables real-time
delivery of products or services and solutions over the Internet. A typical Cloud
Computing service would be the Elastic Compute Cloud from Amazon. Furthermore,
a popular field of Cloud Computing application is called Software as a Service
(SaaS), where software is delivered via Internet or some centralized access points to
the clients rather than installed locally on the user's device.
116
Research variables:
The dependent variables The independent variables
The main variable:
Market acceptance of Cloud Computing
The sub-variables:
Market structure of Cloud Computing.
Price model of Cloud Computing.
Service homogeneity of the IT services.
Usage frequency of the IT services.
In accordance with achieving the aimed goal of this study; this questionnaire is
designed in two parts:
Part one: Include the general information of study Respondents.
Part two: Include the four dimensions of the study, which are:
he first dimension (general information and knowledge of Cloud Computing):
general questions about the company (type of company, IT activities and budget)
and questions about the status quo of Cloud Computing market, including how many
companies among the respondents are already using or plan to use Cloud Computing
services, as well as their opinions on the pros and cons of Cloud Computing
services.
The second dimension (service homogeneity of IT service): questions about the
respondent‘s opinion on the service homogeneity of the IT services they use.
The third dimension (usage frequency of IT service): questions about the
respondents‘ opinion on the Usage frequency of the IT services they use.
The fourth dimension (market structure): this section contains a question about the
respondents‘ preferred market structure.
The fifth dimension (price model): this section contains a question about the
respondents‘ preferred price model.
Thank you for your cooperation
Researcher
Eng. Faten Abu Dagga
117
Part One: Personal Functional Information Please put out the signal (√) in front of the correct answer
1. Gender:
Male
Female
2. Qualification:
Bachelor
Master
PhD
3. Age (in years)
Below 30 years
From 30 – below40
From40 –below50
Above 50 years
4. Field of Specialization
Commerce
Engineering
IT
Other Specify___________
5. Position
Director
Manager
Head of Department
Head of Unit
Engineer Administrator
6. Years of Experience at this company
Less than 5
From 5 – less than 10
From10–less than 15
Above 15 years
7. Department
Technical
Commercial
Financial
Corporate supply chain
Human Resources
118
Part Two: The first dimension (general information and knowledge of Cloud Computing):
***Please put out the signal (√) in front of the correct answer
1. I am familiar with the idea of Cloud Computing.
Strongly Agree
Agree
Disagree
Strongly Disagree
2. How much did your company spend on IT related projects in 2014?
0-1% of 2013 revenue
1%-5% of 2013 revenue
5%-20% of 2013 revenue
>20% of 2013 revenue
Not sure
3. The best description of Cloud Computing's current role for your company is:
We are already using some Cloud Computing services and don‘t expect
more.
We are already using some Cloud Computing services and planning to use
more.
We are planning to use some Cloud Computing services in near future.
We regard Cloud Computing as a vision which won't be implemented in
near future.
Other
119
4. The reason(s) why Cloud Computing seems attractive to your company include(s):
Str
ongly
Agre
e
Agre
e
Nat
ura
l
Dis
agre
e
Str
ongly
Dis
agre
e
Less capital lockup
Less sunk costs and separate capex & opex
Less administration and maintenance costs
High scalability of the system continuity and avilability
Less data loss or other security issues
The interoperability of Cloud Computing services
Quick integration into existing implementations
Less deployment time and complexity
Better monitoring tools and accountability of services
Consolidation of legacy systems
Environment awareness(Green IT)
5. Your concern(s) about using Cloud Computing now or in near future is/are :
Item
Str
ongly
Agre
e
Agre
e
Nat
ura
l
Dis
agre
e
Str
ongly
Dis
agre
e
Technology immaturity
Technology complexity
Potential system failure due to hardware problems
Security issues (data loss, confidential information etc.)
Legacy infrastructure
Legal compliance
High deployment costs
Lock in problem and opportunity cost by following the
wrong trend
Hostile software licensing regime
121
The second dimension (service homogeneity of IT service):
1. Which service homogeneity would you prefer for each of the following IT
service:
Item Homogeneous
service
Heterogeneous
service
No
answer
Storage, archiving and disaster recovery
Raw computing power (CPU, Memory etc)
Dedicated data center or servers (e.g. Dell, HPC etc.)
Basic office applications (e.g. Microsoft Office)
Business applications (e.g. SAP ERP system)
Specialized applications or solutions (e.g. simulation
software for financial industry)
Specialized IT services, such as security, management
and compliance
Cloud Operating System (e.g. Windows Azure from
Microsoft)
Online Application Exchange Platform (e.g.
Salesforce.com)
121
The third dimension (usage frequency of IT service)
1. How frequently does your company use the following IT services?
Item
Ver
y
freq
uen
tly
(man
y t
imes
in a
day
)
Fre
qu
entl
y
(dai
ly)
No
rmal
(dai
ly-
wee
kly
)
Infr
equ
ent
(mo
nth
ly)
Ver
y
Infr
equ
ent
(rar
e)
No
an
swer
Storage, archiving and disaster recovery
Raw computing power (CPU, Memory etc)
Dedicated data center or servers(e.g. Dell,
HPC etc.)
Basic office applications (e.g. Microsoft
Office)
Business applications (e.g. SAP ERP
system)
Specialized applications or solutions (e.g.
simulation software for financial industry)
Specialized IT services, such as security,
management and compliance
Cloud Operating System (e.g. Windows
Azure from Microsoft)
Online Application Exchange Platform (e.g.
Salesforce.com)
122
The fourth dimension (market structure)
1. Which transaction type would you prefer for each of the following Cloud
Computing service:
Item
Short
ter
m
tran
sact
ion
Long t
erm
tran
sact
ion
In-h
ouse
tran
sact
ion
No a
nsw
er
Storage, archiving and disaster recovery
Raw computing power (CPU, Memory etc)
Dedicated data center or servers (e.g. Dell, HPC etc.)
Basic office applications (e.g. Microsoft Office)
Business applications (e.g. SAP ERP system)
Specialized applications or solutions (e.g. simulation
software for financial industry)
Specialized IT services, such as security, management
and compliance
Cloud Operating System (e.g. Windows Azure from
Microsoft)
Online Application Exchange Platform (e.g.
Salesforce.com)
123
The fifth dimension (price model)
1. Which price model would you prefer for each of the following Cloud
Computing service?
Item
Fla
t
Pri
cing
Pay
as
you
go P
rici
ng
Mix
ture
of
Fla
t &
Pay
as y
ou g
o
No a
nsw
er
Storage, archiving and disaster recovery
Raw computing power (CPU, Memory etc)
Dedicated data center or servers (e.g. Dell, HPC etc.)
Basic office applications (e.g. Microsoft Office)
Business applications (e.g. SAP ERP system)
Specialized applications or solutions (e.g. simulation
software for financial industry)
Specialized IT services, such as security, management
and compliance
Cloud Operating System (e.g. Windows Azure from
Microsoft)
Online Application Exchange Platform (e.g.
Salesforce.com)
124
Appendix (C)
Survey Results
Table (1) service homogeneity of IT service
No. Items homogeneity heterogeneity No
answer
1 Storage, archiving and disaster
recovery
79 47 0
2 Raw computing power (CPU,
Memory etc)
70 41 6
3 Dedicated data center or servers
(e.g. Dell, HPC etc.)
66 41 9
4 Basic office applications (e.g.
Microsoft Office)
74 41 4
5 Business applications (e.g. SAP
ERP system)
58 51 1
6
Specialized applications or
solutions (e.g. simulation
software for financial industry)
51 55 44
7
Specialized IT services, such as
security, management and
compliance
64 51 4
8
Cloud Operating System (e.g.
Windows Azure from
Microsoft)
46 58 55
9
Online Application Exchange
Platform (e.g. Salesforce.com)
41 49 56
All items
count 274 188 78
% 50.74 34.81 14.44
125
Table (2) usage frequency of IT service
No. Items
Very
frequently
(many times in
a day)
Frequently
(daily)
Normal(daily-
weekly)
Infrequent
(monthly)
Very
Infrequent(rare)
No
answer
1 Storage, archiving and disaster recovery
30 16 4 4 4 2
2 Raw computing power (CPU,
Memory etc)
19 15 11 1 6 8
3 Dedicated data center or servers
(e.g. Dell, HPC etc.)
12 14 9 6 9 10
4 Basic office applications (e.g.
Microsoft Office)
22 18 11 4 1 1
5 Business applications (e.g. SAP ERP system)
7 9 15 10 10 9
6
Specialized applications or
solutions (e.g. simulation software for financial industry)
8 18 15 6 8 5
7
Specialized IT services, such as
security, management and compliance
10 21 15 5 7 2
8 Cloud Operating System (e.g.
Windows Azure from Microsoft)
4 11 10 5 10 20
9 Online Application Exchange Platform (e.g. Salesforce.com)
1 14 7 10 11 17
All items
count 102 145 114 58 56 65
% 18.9% 26.9% 21.1% 10.7% 10.4% 12.0%
126
Table (3) Market structure
No. Items Short term
transaction
Long term
transaction
In-house
transaction
No
answer
1 Storage, archiving and disaster
recovery 14 15 29 2
2 Raw computing power (CPU,
Memory etc) 21 10 22 7
3 Dedicated data center or servers
(e.g. Dell, HPC etc.) 18 10 27 5
4 Basic office applications (e.g.
Microsoft Office) 16 12 25 7
5 Business applications (e.g. SAP
ERP system) 16 16 19 9
6 Specialized applications or
solutions (e.g. simulation software
for financial industry)
9 12 27 12
7 Specialized IT services, such as
security, management and
compliance
13 11 29 7
8 Cloud Operating System (e.g.
Windows Azure from Microsoft) 13 9 16 22
9 Online Application Exchange
Platform (e.g. Salesforce.com) 12 9 14 25
All
items
count 132 128 210 70
% 24.4% 23.7% 38.9% 13.0%
127
Table (4) Price model
No. Items Flat
Pricing
Pay as
you go
Pricing
Mixture
of Flat &
Pay as
you go
No
answer
1 Storage, archiving and disaster
recovery 25 22 12 1
2 Raw computing power (CPU,
Memory etc) 19 23 14 4
3 Dedicated data center or servers
(e.g. Dell, HPC etc.) 22 23 14 1
4 Basic office applications (e.g.
Microsoft Office) 18 27 13 2
5 Business applications (e.g. SAP
ERP system) 11 25 17 7
6 Specialized applications or
solutions (e.g. simulation software
for financial industry)
12 25 19 4
7 Specialized IT services, such as
security, management and
compliance
25 26 6 3
8 Cloud Operating System (e.g.
Windows Azure from Microsoft) 9 20 13 18
9 Online Application Exchange
Platform (e.g. Salesforce.com) 13 18 13 16
All
items
count 159 217 118 46
% 29.4% 40.2% 21.9% 8.5%
128
Table (5) The best description of Cloud Computing's current role for your
company
The best description of Cloud Computing's current role
for your company Frequency Percentages
We are already using some Cloud Computing services
and planning to use more 28 46.7
We are planning to use some Cloud Computing services
in near future
17 28.3
We are already using some Cloud Computing services
and don‘t expect more 7 11.7
We regard Cloud Computing as a vision which won't be
implemented in near future
5 8.3
Other 3 5.0
Total 60 100.0
Table (6) why Cloud Computing seems attractive to your company include
No. Items Mean standard
deviation
Weight
mean
t-
value
P-
value
4 High scalability of the
systemcontinuity and avilability
4.72 4.207 94.33 3.161 0.002
1 Less capital lockup 4.20 0.819 84.00 11.346 0.000
7 Quick integration into existing
implementations
4.13 5.482 82.67 1.601 0.115
2 Less sunk costs and separate
capex&opex
3.88 0.761 77.67 8.989 0.000
3 Less administration and
maintenance costs
3.88 0.904 77.67 7.571 0.000
8 Less deployment time and
complexity
3.52 1.066 70.33 3.756 0.000
6 The interoperability of Cloud
Computing services
3.47 0.833 69.33 4.340 0.000
9 Better monitoring tools and
accountability of services
3.37 0.920 67.33 3.087 0.003
11 Environment awareness(Green
IT)
3.35 0.988 67.00 2.743 0.008
5 Less data loss or other security
issues
3.18 1.242 63.67 1.144 0.257
10 Consolidation of legacy systems 3.03 1.149 60.67 0.225 0.823
All items 3.70 0.895 74.06 6.084 0.000
129
Table (7) using Cloud Computing now or in near future
No. Items Mean standard
deviation
Weight
mean
t-
value
P-
value
1 Technology immaturity 3.72 0.976 74.33 5.689 0.000
2 Technology complexity 3.55 1.016 71.00 4.195 0.000
3
Potential system failure due to
hardware problems 3.58 0.829 71.67 5.448 0.000
4
Security issues (data loss,
confidential information etc.) 3.72 1.010 74.33 5.496 0.000
5 Legacy infrastructure 3.45 0.872 69.00 3.998 0.000
6 Legal compliance 3.50 0.873 70.00 4.435 0.000
7 High deployment costs 3.27 1.177 65.33 1.755 0.084
8
Lock in problem and
opportunity cost by following
the wrong trend
3.23 1.047 64.67 1.725 0.090
9
Hostile software licensing
regime 3.50 1.066 70.00 3.634 0.001
All items 3.50 0.486 70.04 8.005 0.000
131
Appendix (D)
Table 1 Companies that participate in the study. No Company Name Contact Name Mobile E-Mail Tel. Tel City
1 AL-Qudwa Company Ahmad alqudwa 599-999919 info@alqudwa
.ps
972-8-
2827717 972-8-2823933 Gaza
2 ALTARIQ Systems &
Projects Tarek M. Eslim 599-529295
tarek@altariq.
ps, tarek@p-i-
s.com
970-8-
2860280 970-8-2847736 Gaza
3 Bisan Tech for Systems &
Communications Ltd
Haitham AL
Khateeb 599-677904
Haitham@Bis
anTech.ps
970-8-
2888719 970-8-2888709 Gaza
4 BeOnline
5 Castle Establishment
Company Majdy Abu Daff 594-35450
castle@castles
oft.net
970-8-
2833211 970-8-2846885 Gaza
6 citynet Majdi Almaqadma 599-417329 [email protected]
s
970-8-
2821373 970-8-2864715 Gaza
7 Computer Connect Mohamed Abu
Nahla 599-602545
m.ali@connec
t.ps
970-8-
2843387 970-8-2882213 Gaza
8 Computer Land Center Merwan Kehail 599-855662 info@compute
rland.ps
970-8-
2852229 970-8-2855662 Gaza
9
Development Pioneers
Company for
Consultations
Wessam Suliman
Al
Moamer
589-763179 info@pioneer.
ps
972-8-
2888781 972-8-2888781 Gaza
10 Effects For Consultations
and Development Nahed Eid 599-988776
s
970-2-
2233445 970-2-2233445 Gaza
11
Fusion for Internet services
and TeleCommunication
systems
Khaled Abu Hasna 599-626323 [email protected]
s
970-2-
2977439 970-8-2880158 Gaza
12 Future Information
Systems Jihad Kaloub 594-07724 [email protected]
972-8-
2820207 972-8-2820065 Gaza
13 Future Tech Mohamad El-Alami 594-1234 alamim@futur
etech-pal.com
970-8-
2835655 970-8-2847355 Gaza
14 Impact Consulting, Inc. Rami A. Wihaidi 599-224084 rami.wihaidi@
impact.ps
970-8-
2827777 970-8-2827777 Gaza
15
Jamal Sons Telecom
Computer
Systems Ltd.
Mohammed Jamal
Salem Haboush 595-00600
jamal@jamals
ons.com
970-8-
2833507 970-8-2867199 Gaza
16 jerusalem information
technology ayman h. bakroun 599-424141
ayman@jit-
co.ps
970-8-
2824446 970-8-2824445 Gaza
17 johatoon for cartoon Omima Joha 599-865227 info@johatoo
n.ps
970-8-
2843197 970-8-2843197 Gaza
18 Link Information
Technolojy
Hazem Zyad Al
Asaly
598-295031 hazem@linkit.
ps
970-8-
2825520 970-8-2825530 Gaza
19
Mdar Co. for management
and
software
Munis Ahmed 599-064276 [email protected] 972-8-
2862338 972-8-2862338 Gaza
20 Modern Tech Corporation
(MTC)
Rassem Fayez
Mushtaha 599-408843
mtcg@mtcgaz
a.com
970-8-
2820929 970-8-2824099 Gaza
21 Nepras for Media and IT Fady Issawi 599-494971 fady.issawi@n
epras.com
970-8-
2835933 970-8-2820332 Gaza
22 netstream Ziad Elshikhdeeb 599-479195 ziad.deeb@net
stream.ps
972-8-
2883900 972-8-2883900 Gaza
23
P A L I N V E S T® -
Development and Business
Services
Ahmed F. ElFarra 598-182222 aelfarra@palin
vest.ps
970-8-
2889777 970-8-2889776 Gaza
24
Palestine For
Communication
& IT
Dr. Mahir B. Sabra 599-600043 msabra@pcit.
ps
972-8-
2889129 972-8-2889129 Gaza
131
25
PC WORLD COMPANY
LTD
AHMED-RAMI Y
ABU ELOUN
594-07670
rami@pcworl
d-co.com
970-8-
2825968
970-8-2824229
Gaza
26 SADAF Technology
Development
Mohammed
Alafranji 599465222 [email protected]
970-8-
2843388 970-8-2888821 Gaza
27
Sidata Information and
Communication Systems
Ltd.
Fawaz Khaled El-
Alami 599716106 [email protected]
970-8-
2824665 970-8-2825131 Gaza
28 Speed Click for IT & Tele
Communications Ltd.
Wael Mohammed
Hamdy Nabhan 599-601602
wael@speedcl
ick.ps
970-8-
2886004 970-8-2886004 Gaza
29 TATWEER Business
Services
Haitham Abu
Shaaban 599-479209
haitham.abush
aaban@tatwee
r.ps
970-8-
2882700 970-8-2882600 Gaza
30 Teletalk Telecom Co.Ltd Talal T. Khalil 598-280028 Info@teletalk.
ps
970-2-
2977445 970-8-2881123 Gaza
31 Unit One ICT
Saady S. Lozon 599-750531
info@unitone.
ps
972-8-
2843130 972-8-2883607 Gaza
32 VISION PLUS Ashraf Elyazouri 599-526119 info@visionpl
us.ps
970-8-
2888776
970-8-2884888 Gaza
33 Ziyad Mourtaga & Bros.
Co. ashraf demaidi 599-600666
info@z-
mourtaga.ps
970-8-
2867593 970-8-2866562
Gaza
132
Appendix (E)
Referees Who Judge the Reliability of the questionnaire
No. Name Position
1
Prof. Dr. Yousef Ashour Professor at Commerce College - IUG
2 Prof. Dr. Faris Abu Mouamar Professor at Commerce College - IUG
3 Dr. Wassim Al Habil Associate Professor at Commerce
College - IUG
4 Dr. Sami Abou-Al-Ross Assistant Professor at Commerce
College – IUG
Dr. Nafez Barakat Assistant Professor at Commerce
College – IUG
5 Dr. Ayman Abu Samra Assistant Professor at Engineering
College – IUG
6 Dr. Mohamed Al Hanjouri Assistant Professor at Engineering
College – IUG